Integral Index of Traffic Planning: Case-Study of Moscow City's

Home , Boulevard Ring, Garden Ring, Transportation in Moscow

sustainability

Article Integral Index of Traﬃc Planning: Case-Study of Moscow City’s Transportation System

Tatiana Petrova 1,* , Andrey Grunin 1 and Arthur Shakhbazyan 2

1 Faculty of Physics, Lomonosov Moscow State University, Leninskiye Gory, 1, 199991 Moscow, Russia; [email protected] 2 Moscow Traﬃc Management Center, Zolotorozhskiy Val, 4 bld. 2, 111033 Moscow, Russia; [email protected] * Correspondence: [email protected]

Received: 4 June 2020; Accepted: 5 September 2020; Published: 9 September 2020

Abstract: The integral indexes are used to measure trends and monitor progress in transportation complex development. The selection of the indicators, included in indexes, is related to the data availability (depends on existence of a specific data sources). The aim of this paper is to provide a development methodology of Integral Index of Traffic Planning (Integral TP Index), which is based on the primary data on vehicle speeds, traffic volumes, number of accidents, etc., for Moscow and allows for basic assessment of transport situation in Russian’s capital. The proposed methodology is a combination of economic and urban approaches to analyze the key indicators of transportation planning efficiency in the metropolis. Four groups of indicators are considered: traffic management efficiency, traffic management quality, transit efficiency and road safety. The integral index considers traffic volumes for various roads and their contributions to the overall transportation system. Division of streets by type (highways, rings, center) makes it possible to take into account specifications the radial-ring streets structure of Moscow. The constructed index is applied to the analysis of Moscow transportation statistics in 2012–2017 provided by the Moscow Traffic Management Center, Yandex and TomTom.

Keywords: transportation planning; urban mobility; traffic efficiency; traffic flow; road traffic analysis; traffic index; traffic volume; urban transport

1. Introduction The urban transportation complex is a dynamically changing system, influenced by many factors, including construction of new interchanges and roads, changes in carriageway and footway widths, implementation of paid parking spaces, bus lanes and bike paths, etc., (Yandex [1]). Integral indexes are frequently used to assess the level of transportation complex development (Litman [2]). Such indexes are developed by official statistical agencies or other public authorities, international and national research centers, independent analytical companies; they become an important criterion for assessing life quality in cities, to be included into global competitiveness ratings of countries and regions. Indexes are also highly instrumental for the assessment of transport situation in a city or a country in time. Significant global experience was accumulated in integral indexes. The selection of the indicators, included in indexes, is related to the scope of the index and data availability (depends on existence of a specific data sources). The specialized indexes are: the Transportation Services Index [3], Logistics Performance Index [4] and Emerging Markets Logistics Index [5] rank the freight and logistics sectors; Urban Mobility Index [6] and The Siemens’ Green Cities Index [7]—ecological situation; Overall

Sustainability 2020, 12, 7395; doi:10.3390/su12187395 www.mdpi.com/journal/sustainability Sustainability 2020, 12, 7395 2 of 23

Global Mobility Ranking [8] and The Deloitte City Mobility Index [9]—mobility level; TomTom Traffic Index [10], INRIX [11] and Travel Time Index [12]—traffic congestion. The indexes are based on indicators, grouped by different types and categories. For the purposes of Integral Index of Traffic Planning construction, the most important are transport and mobility indicators. Casey and Norwood [13] define mobility by using several key groups of indicators: the travel time, lost time, reliability of the system, the status of the transport system and travel costs. The optional indicators are: congestion-related travel time, quantity of trips, modal split, transit time required and transit efficiency. In Imran and Low [14] the transport indicators are related to the non-motorized transport, car ownership, average travel time, traffic volume and vehicle status. Barker et al. [15] analyses the sustainability of the transport system and mobility using the indicators which are defined in terms of travel time i.e., congestion, transport-generated expenditure, casualties of traffic accidents, energy consumption and polluting emissions. The key indicator is the number of vehicles–kilometers. For the needs of assessing the urban mobility in Lyon [16], a set of indicators is defined to create a relevant and simple system, connected to the existing statistical database. The indicators are grouped in four categories, related to the mobility, economy, society and environment. The mobility indicators include: the number of trips, travel time, travel purpose, modal split, daily average distance travelled by the motorized and non-motorized trips and average speed. Kaparis and Bell [17,18] defined a set of indicators intended for the traffic management and the Intelligent Transport Systems. The group of indicators called “Traffic Efficiency” deals with mobility defined by 14 indicators: the average duration of a road trip to the appropriate point of interest, the average duration of trips to the appropriate point of interest carried out by using the public passenger transport system, the capacity/supply of the public transport system, the time required to switch between transport modes, the average distance between different transport modes, the time required to access the station, the average duration of a search for a parking place, the average duration of the daily trips, the average distance covered by the daily trips, the total length of the road network, the coverage of the road network by ITS services, the modal split, the share of non-motorized trips in daily commuting and the length of the transport network intended for non-motorized trips. In Casey and Norwood [13] six frequently cited areas of mobility measures are highlighted. There are: congestion related (e.g., level of service, volume/capacity, and delay); trip time; amount of travel (vehicle miles traveled, vehicle hours traveled); mode share; transfer time; and transit performance. For developing the Traffic Planning index for Moscow, we applied frequently used and cited transport and mobility indicators and measures, supplemented them with more specific indicators and aggregated all indicators in four groups: traffic management efficiency, traffic management quality, transit efficiency and road safety. Each group is described by a core subindex consisting of several source metrics. We also modified some of the standard indicators and measures to take into account local specifications of Moscow (such as radial-ring streets structure, huge traffic volume, significance of road accidents number) and availability of primary data for index constructing. The structure of Integral TP Index is: Subindex 1. Traffic management efficiency:

Metric 1.1. Traﬃc volume, Metrics 1.2., 1.3 Normalized spatial and temporal speed dispersion.

Subindex 2. Traﬃc management quality:

Metric 2.1. Average speed for diﬀerent time intervals and road types.

Subindex 3. Transit eﬃciency:

Metric 3.1. Travel time on diﬀerent routes for diﬀerent departure time intervals.

Subindex 4. Road safety: Sustainability 2020, 12, 7395 3 of 23

Metrics 4.1., 4.2. The probability of fatal or non-fatal injury in an accident.

Higher value of each subindex corresponds to improving of the direction, described by subindex. To account for the contribution of various roads to the integral road system, the subindexes 1, 2 and 4 use the traffic volumes. The splitting of streets by type (highways, rings, center) and different transit routes was used in subindexes 2 and 3, respectively, and allows to consider the special case of Moscow radial-ring road structure. The significance of road accidents was considered in subindex 4.

1.1. Subindex 1. Traffic Management Efficiency Subindex 1 describes the traffic management efficiency. It shows how many vehicles a city can accommodate on average per day and how efficiently traffic flows are distributed in space and in time of the day. The subindex 1 evaluates long-term decisions (for example, roads planning), rather than short-term road events (accidents, short-term repairs, etc.). To assess the traffic management efficiency, several source metrics are used: traffic volume and speed dispersion (spatial and temporal).

1.1.1. Traffic Volume Traffic volume (also referred to as intensity) is traditionally defined by the “average number of vehicles (n) that pass a cross-section during a unit of time (T)” (Hoogendoorn and Knoop [19]). Dynamics of this indicator allows to account for events impacting the patterns of traffic, but not the average speed value. The traffic volume is mentioned as one of the indicators in Casey and Norwood [13], Imran and Low [14] and used, for example, in INRIX [11]. In our study the traffic volume is one of the key parameters for averaging data over ensembles of roads (for example, calculation of speed for all roads, city center roads, rings, and highways). We use the total traffic volume for all city roads expressed in the total number of vehicles passing through the city per day as an additional metric for subindex 1 “The effectiveness of traffic management”.

1.1.2. Speed Dispersion Another reference indicator used to assess the traffic management efficiency and directly reflecting the vehicles distribution uniformity on the roads is speed dispersion. Aarts et al. [20] and Rogdriguez [21] establish the relationship between dispersion and the probability of road traffic accidents. In Aarts et al. [20] it was shown that the probability of accidents correlates with average flow speed and dispersion of speeds in the flow. Casey and Norwood [13] determined the influence of the average flow speed, individual vehicle speed and dispersion of speed on the probability of death in an accident. It was shown that increase of flow dispersion matches the increase in the probably of accidents leading to fatal injuries on the road. Our study uses data on average flow speeds for different time intervals and in different roads sections, without details of individual vehicles speeds. We considered two types of dispersion. The first type is temporal speed dispersion, illustrating the uniformity of various streets traffic during the day. The second one is spatial dispersion, which shows how evenly traffic is distributed along the city roads in a fixed time period for each hour of the day. The lower dispersion value corresponds to more uniform traffic flow distribution. The speed dispersion is used in connection with the average speed values, considering the normalized dispersion as the ratio of the absolute dispersion value to the average speed squared.

1.2. Subindex 2. Traffic Management Quality The subindex 2 “Traffic management quality” describes the mathematical expectation of city travel time (average traffic speed) and its variation depending on time of the day (rush hour, daytime, etc.) and road type (rings, highways, central roads). Similar subindexes are used in both theoretical and practical research internationally. Most often, the average flow speed is compared to similar benchmarks, e.g., rush hour speed, free flow speed, etc. Sustainability 2020, 12, 7395 4 of 23

The traffic management quality (average speed for different time intervals) is included in the following set of indexes: TomTom Traffic Index [10], INTRIX Global Traffic Index [11], Yandex.Navigator [22], etc. Feifei et al. [23] proposes an index based on average speed measurements (“Speed performance index”). To estimate the traffic quality, the ratio of average speed to the maximum allowed road speed was used. This index evaluates the traffic congestion degree on a four-point scale with four points corresponding to the highest congestion. Su et al. [24] explores the metric consisting of average speed weighted by the total number of vehicles. The results are divided into six groups to define different road regimes, from “traffic jam” to “free flow”. In our study the” free flow” speed (i.e., the average night speed) is used to construct the subindex 2 “Traffic management quality”. This methodology is similar to TomTom Traffic Index [10].

1.3. Subindex 3. Transit Efficiency Transit efficiency is represented by travel time for given key routes. The higher it is, the more time citizens lose every day. The core metric of this subindex is a special case of Hansen Accessibility Index proposed in Hansen [25]. Taylor [26,27] enriched this indicator by adding economic parameters such as the average cost of delay minute, etc., and considered additional metrics related to transit efficiency: congestion index, proportion of stops time, adjustment for acceleration. The travel time indicator is also mentioned in Casey and Norwood [13], Lyon [16], Kaparis and Bell [17,18]. Our study focuses on key Moscow’s routes: from North to South, and from West to East via the Outer Ring Road, via the Third Transport Ring and typical highways. We also consider travel time from North, South, East, and West to the city center, represented by the Moscow Kremlin.

1.4. Subindex 4. Road Safety Road safety is one of the key parameters of a transportation system. Safety can be quantitatively and qualitatively defined as the frequency and significance of traffic accidents. In Kaparias and Bell [18], the frequency of accidents was used as a key quantitative indicator of safety. In our study, the subindex “Road safety” is based on the parameters defining the probability of fatal or non-fatal injury in road accidents. These parameters are calculated using traffic volume and the number of traffic accidents resulting in injury or death. The approach is related to indicator “casualties of traffic accidents” proposed by Barker et al. [15].

2. Materials and Methods Based on the analysis and systematization of international research experience in the ﬁeld of transport analytics, presented in Section1, four aspects are covered in the Integral TP Index:

Subindex 1. Traffic management efficiency; Subindex 2. Traffic management quality; Subindex 3. Transit efficiency; Subindex 4. Road safety.

Each subindex is a set of core metrics discussed in Section3. Our study relies on datasets for the key roads connecting diﬀerent city areas: the main radial highways, ring roads and city center roads. Various indicators were analyzed on 63 key roads in two directions (“to the city center” and “from the city center” for radial highways, “outer part” and “inner part” for ring roads) for two basic three-week periods each year from 2012 to 2017 (end of September–mid-October (“autumn”), end of April–mid-May (“spring”)) and various road types (Sections 2.2 and 2.3). The choice of radial highways and ring roads was determined by the speciﬁcs of the radial-ring structure of Moscow (Schmidt [28]). The key roads are used by Moscow citizens most often, they cover uniformly all city districts, uniformly present all road types and the available datasets of these roads are the fullest. Sustainability 2020, 12, 7395 5 of 23 Sustainability 2020, 12, x FOR PEER REVIEW 5 of 23

2.1. Data:The data Sources were and provided Format by the Moscow Traffic Management Center, Yandex and TomTom for pre-agreedThe data dates were of provided2012–2017 by and the streets Moscow within Traffi thec ManagementMoscow Outer Center, Ring Road Yandex including and TomTom the Outer for pre-agreedRing Road. dates The data of 2012–2017 of road accidents and streets were within provided the Moscow for 2013–2017. Outer Ring The Road data includingformat varied the Outer from Ringsource Road. to source, The data as described of road accidents in Appendix were A. provided for 2013–2017. The data format varied from source to source, as described in AppendixA. 2.2. Research Data Periods 2.2. ResearchThe data Data covered Periods two periods each year from 2012 to 2017: • The3 weeks: data coveredend of Septembe two periodsr–mid-October each year from (“autumn”); 2012 to 2017: • 33 weeks:weeks: endend ofof September–mid-OctoberApril–mid-May (“spring”). (“autumn”); • 3 weeks: end of April–mid-May (“spring”). • For the “autumn” period, the number of vehicles is close to the average annual as it follows the end Forof vacation the “autumn” period period, in summer, the number while of winter vehicles season is close limitations to the average (ice and annual snow as iton follows the roads) the end do ofnot vacation have a period significant in summer, effect while yet. winterThe second season limitationsperiod (“spring”) (ice and snowincludes on the long roads) spring do not holidays have a significanttraditional e forffect Russia, yet. The when second the periodcitizens (“spring”) tend to le includesave the longcity for spring vacation. holidays For traditional this “spring” for Russia,period whenthe number the citizens of vehicles tend to in leave the thecity city decreases for vacation. and so For the this period “spring” was periodchosen thefor numbercomparison of vehicles as being in therelatively city decreases uncongested. and so the period was chosen for comparison as being relatively uncongested.

2.3.2.3. RoadRoad ObjectsObjects TypesTypes MoscowMoscow metropolitanmetropolitan road road network network is primarilyis primarily deﬁned defined by the by radial-ring the radial-ring street structure,street structure, similar tosimilar some to major some European major European cities. To accountcities. To for accoun such structure,t for such the structure, network the objects network were dividedobjects were into typesdivided as followsinto types (Figure as follows1): (Figure 1):

(a) (b) (c) (d)

FigureFigure 1.1. The scheme scheme of of Moscow Moscow roads. roads. The The roads roads considered considered in inthe the study study are are highlighted highlighted in red. in red. (a) (Thea) The main main highways highways (Lipetskaya (Lipetskaya Street, Street, Mozhaysk Mozhayskoyeoye Highway, Highway, Prospect Prospect Mira, Mira, Ryazansky Ryazansky Prospekt, KashirskoyeKashirskoye Highway, Highway, Andropova Andropov Prospekt,a Prospekt, Lublinskaya Lublinskaya Street, Yaroslavskoye Street, Yaroslavskoye Highway, Vernadskogo Highway, Prospekt,Vernadskogo Volgogradskiy Prospekt, Volgogradskiy Prospekt, Altufevskoye Prospekt, Highway,Altufevskoye Michurinsky Highway, Prospekt,Michurinsky Rublevskoye Prospekt, Highway,Rublevskoye Leningradskoye Highway, Leningradskoye Highway, Shchelkovskoye Highway, Shchelkovskoye Highway, Profsoyuznaya Highway, Profsoyuznaya Street, Dmitrovskoye Street, Highway,Dmitrovskoye Warsawskoye Highway, Highway, Warsawskoye Zvenigorodskoye Highway, Highway,Zvenigorodskoye Marshal ZhukovHighway, Avenue, Marshal Enthusiasts Zhukov Highway,Avenue, Enthusiasts Volokolamskoye Highway, Highway); Volokolamskoye (b) Ring roads Highway); (The Outer (b) Ring Ring Road, roads The (The Third Outer Transport Ring Road, ring, TheThe GardenThird Transport Ring, The ring, Boulevard The Gard Ring);en Ring, (c) City The center Boulevard roads (insideRing); ( thec) City Garden center Ring, roads including (inside thethe GardenGarden Ring);Ring, including (d) All the the main Garden streets Ring); of the(d) city.All the This main type streets includes of the streets city. This included type includes in types streets (a–c), asincluded well as in streets types not (a– includedc), as well in as these streets types. not included in these types.

TheThe datadata of of road road sections sections were were implemented implemented for Subindexfor Subindex 3. “Transit 3. “Transit efficiency” efficiency” calculation calculation when routewhen contained route contained only part only of part the road.of the road. 2.4. Time Intervals 2.4. Time Intervals The following time periods were defined for time-dependent metrics: The following time periods were defined for time-dependent metrics: 24-h period; •• 24-h period; Morning rush hour (7.00 a.m.–9.59 a.m.); •• Morning rush hour (7.00 a.m.–9.59 a.m.); • Evening rush hour (4.00 p.m.–7.59 p.m.); • Evening rush hour (4.00 p.m.–7.59 p.m.); • Daytime period (11.00 a.m.–2.59 p.m.); • Daytime period (11.00 a.m.–2.59 p.m.); • Night period—“free flow” (1.00 a.m.–4.59 a.m.). • Night period—“free flow” (1.00 a.m.–4.59 a.m.).

Sustainability 2020, 12, 7395 6 of 23

2.5. Key Values Calculation

2.5.1. Average Speed Calculating the average vehicles speed metric requires estimation of average speed for road ensembles (for example, for a group of roads called “rings”). To solve it, we used traffic volume: the contribution of different roads to the total road traffic was taken proportionally to the road (road section) traffic volume: PN LiIi v = i=1 PN (1) i=1 tiIi where

Ii, Li, ti —the traﬃc volume, length and travel time of the i-th road section, respectively, N—number of road sections.

When data on the traﬃc volume were not available, the assessment was based on the number of lanes in a given road section.

2.5.2. Speed Dispersion Two types of dispersion were defined. The first one was called “spatial dispersion” (the time interval was fixed and the road sections were changed). The spatial dispersion for time period 6.00–6.59 is 2 PN ( j 2012 2012 ) j=1 v6.00 v 6.00 D 2012 spatial = − (2) 6.00 N j 2012 v6.00 —speed on the j-th road section in time interval 6.00–6.59 for 2012 year; 2012 v 6.00—the average speed for all road sections in time interval 6.00–6.59 for 2012 year; N —number of road sections.

The same procedure was made for 24 1-h time intervals and for all available years. The second one is “temporal dispersion” (the road section was ﬁxed and the time intervals were changed). The following formula was used for calculating the temporal speed dispersion for Enthusiasts Highway to the center (EHc):

PT i 2012 2012 2 i=1 (v v EHc) D temporal = EHc − (3) EHc T

i 2012 vEHc —speed for the i-th time interval for Enthusiasts Highway to the center (EHc) for 2012 year, 2012 v EHc—the average speed for all time intervals for Enthusiasts Highway to the center (EHc) for 2012 year, T —number of time intervals.

The same procedure was made for all available road sections for all available years. For the purpose of comparison of dispersion with the square of average 24-h speed for all road sections, the dispersions were normalized:

spatial 2012 spatial_norm D6.00 D6.00 = (4) 24h 2 (v 2012)

temporal 2012 temporal_norm DEHc DEHc = (5) 24h 2 (v 2012)

24h Here, v 2012 is the average 24-h speed in 2012 year for all road sections. Sustainability 2020, 12, 7395 7 of 23

Sustainability 2020, 12, x FOR PEER REVIEW 7 of 23 2.5.3. Probability of Non-Fatal or Fatal Injury in an Accident 2.5.3.Two Probability reference of valuesNon-Fatal were or used Fatal to Injury assess in tra anﬃ Accidentc safety: the probabilities of receiving a non-fatal and a fatal (caused death) injury in an accident. Two reference values were used to assess traffic safety: the probabilities of receiving a non-fatal The probability of fatal injury is described by the ratio: and a fatal (caused death) injury in an accident.

The probability of fatal injury is described by the fratio: atal N P f atal = 24h (6) 𝑁 𝑃 = I24h (6) 𝐼 f atal N Here,Here, 𝑁 —the24h —the average average number number of deaths of deaths in road in road accidents accidents for the for 24- the hour 24-h in 3-weeks period for for allall roads: roads: P21 f atal = N f atal ∑j 1 j N= 𝑁 (7) 𝑁24h = 21 (7) 21 where I24h is the average traﬃc volume for the 24 h in 3-week periods for all roads. where 𝐼 is the average traffic volume for the 24 h in 3-week periods for all roads. P ∑21 𝐼I j=1 24h I𝐼== (8)(8) 24h 21 here, j is the day in 3-week period. here, j is the day in 3-week period. A similar relation works for the probability of a non-fatal injury in an accident. A similar relation works for the probability of a non-fatal injury in an accident.

2.5.4.2.5.4. Duration Duration of of Morning Morning and and Evening Evening Rush Rush Hour, Hour, Daytime Period TheThe main main characteristic characteristic for definition deﬁnition of rush ho hoursurs is is the the average average vehicles vehicles speed speed in in the the congested congested timetime periods.periods. TheThe following following assumption assumption was was used: us oned: weekdays on weekdays highways highways are most are congested most congested towards towardsthe city centerthe city in center the morning, in the morning, and from and the cityfrom center the city in thecenter afternoon. in the afternoon. The assumption The assumption is explained is explainedby the tendency by the for tendency most citizens for most to live citizens in residential to live in areas residential and in theareas city and region, in the and city workplaces region, and are workplacesmostly located are closemostly to located the city close center. to the city center. TheThe average speed from 6.00 a.m. to to 10.59 10.59 p.m. was was analyzed analyzed for for highways highways along along the the directions directions “to the city center” and “from thethe citycity center”center” (Figure(Figure2 2).).

Morning rush hour Evening rush hour 65 65 60 60 55 55 50 50 45 45 40 40 km/h km/h 35 35 30 30 25 25 20 20 15 15 6a.m. 7a.m. 8a.m. 9a.m. 1p.m. 2p.m. 3p.m. 4p.m. 5p.m. 6p.m. 7p.m. 8p.m. 9p.m. 6a.m. 7a.m. 8a.m. 9a.m. 1p.m. 2p.m. 3p.m. 4p.m. 5p.m. 6p.m. 7p.m. 8p.m. 9p.m. 10a.m. 11a.m. 12p.m. 10p.m. 10a.m. 11a.m. 12p.m. 10p.m. 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017

(a) (b)

FigureFigure 2. TheThe average speed for each hour from 6 a.m. till 10 p.m. on weekdays, 2012–2017. ( (aa)) For For highwayshighways in in the the direction direction to to the the center center (t (thehe definition deﬁnition of of morning morning rush rush hour); hour); ( (bb)) for for highways highways in in the the directiondirection from the center (the de deﬁnitionfinition of evening rush hour).

ItIt isis shownshown that that the the morning morning rush rush hour hour begins begins at about at about 7 a.m. and7 a.m. lasts and until lasts 10 a.m.,until with 10 a.m., maximum with maximumtraffic reached traffic at 8.00–8.59reached at a.m. 8.00–8.59 (Figure a.m.2a). The(Figure evening 2a). rushThe evening hour begins rush at hour about begins 4.00 p.m. at about and lasts4.00 p.m.until and 7.59 lasts p.m., until maximum 7.59 p.m., traffi maximumc reaches attraffic 6.00–6.59 reaches p.m. at (Figure6.00–6.592b). p.m. Therefore, (Figure for 2b). the Therefore, purposes for of the purposes of this research, the morning rush hour was defined from 7.00 a.m. till 9.59 a.m., evening rush hour—from 4.00 p.m. till 7.59 p.m., the daytime period—from 11.00 a.m. to 2.59 p.m. Sustainability 2020, 12, 7395 8 of 23 this research, the morning rush hour was defined from 7.00 a.m. till 9.59 a.m., evening rush hour—from 4.00 p.m. till 7.59 p.m., the daytime period—from 11.00 a.m. to 2.59 p.m.

2.6. Methodology of the Integral Traﬃc Management Index Constructing The subindexes used for constructing the Integral Traﬃc Management Index of metropolis (QIntegral) are listed in the Table1.

Table 1. Subindexes and metrics included in the Integral Traﬃc Planning Index. Each subindex answers the main questions listed in “Questions” column.

# Subindex Definition Included Metrics Questions 1.1. Traffic volume, How many vehicles city Traffic 1.2., 1.3 Normalized accommodates on average? 1 management QTM E f f iciency speed dispersion How efficiently are traffic flows efficiency (spatial and temporal distributed in space (city dispersion) streets) and in time (hours)? What is the average car travel speed one can expect Traffic 2.1. Average speed for depending on departure time 2 management QTM Quality different time intervals and road type? quality and types of roads What is the average speed reduction caused by traffic congestion? 3.1. Travel time on How long will it take to travel different key routes for through the city by 3 Transit efficiency QTransit E f f iciency different departure different routes? time intervals Which route is the fastest? What is the daily probability of 4.1., 4.2. The being died in a road accident? 4 Road safety QSa f ety probability of injury or What is the daily probability of death in an accident being injured in a road accident?

Subindex 1. Traﬃc management eﬃciency (QTM E f f iciency):

Metric 1.1. Traﬃc volume; Metric 1.2., 1.3 Normalized speed dispersion (spatial and temporal dispersion).

Subindex 2. Traﬃc management quality (QTM Quality):

Metric 2.1. Average speed for diﬀerent time intervals and types of roads.

Subindex 3. Transit eﬃciency (QTransit E f f iciency):

Metric 3.1. Travel time on diﬀerent key routes for diﬀerent departure time intervals.

Subindex 4. Road safety (QSa f ety):

Metric 4.1., 4.2. The probability of injury or death in a road accident.

The Integral Traﬃc Management Index of metropolis for each year was calculated as average of subindexes:

QIntegral = (QTM E f f iciency + QTM Quality + QTransit E f f iciency + QSa f ety)/4 (9)

The base of counting the subindexes with the same weight is given in the Section4. “Discussion”. Sustainability 2020, 12, 7395 9 of 23

2.6.1. Traffic Management Efficiency For constructing the traffic management efficiency subindex (QTra f f ic E f f iciency), the values of temporal and spatial dispersion, as well as relative traffic volume changes, were used: Qdisp temporal + Qdisp spatial QTM E f f iciency = + ∆Qvolume (10) 2

In the contributions of the spatial (Dspatial_norm) and temporal (Dtemporal_norm) dispersion (calculated according to Equations (4) and (5) to the subindex a lower value of the normalized dispersion corresponds to a more uniform traffic: Qdisp temporal = 1 Dtemporal_norm 10 (11) − ∗ Qdisp spatial = 1 Dspatial_norm 10 (12) − ∗ An additional score is assigned to the traffic management efficiency subindex each year depending on the relative traffic volume changes of the year compared to the previous one:

Ij+1 Ij ∆Qvolume = − 10 (13) (Ij+1+Ij) ∗ 2

Ij—traffic volume for the j-th year. ∆Qvolume—traffic volume for the first year of observations (2012) was assumed equal to 0.

2.6.2. Traffic Management Quality Traffic management quality subindex is comprised of the average speed values, including 24-h average, daytime and rush hours averages: Q24h + Qdaytime hours + Qrush hours QTM Quality = (14) 3 The averaging of these three components allows to consider congestion related delay for different travel times and all day. The discussion of Traffic management quality components is presented in Section4. For all speed values included in the subindex Q24h, Qdaytime hours , Qrush hours , normalization to the “free flow” speed (night-time speed - departure from 1 a.m. to 4.59 a.m.) was used. So, for the average 24-h speed: v24h Q24h = 10 (15) v f ree f low ∗ v24h—average 24-h speed; v f ree f low—“free flow” speed.

2.6.3. Transit Eﬃciency The transit eﬃciency subindex is based on the three main departure intervals: 7.00 a.m.–7.59 a.m., 12.00 p.m.–12.59 p.m., 5.00 p.m.–5.59 p.m.:

(Q7.00 + Q + Q ) QTransit E f f iciency = 12.00 17.00 (16) 3 Sustainability 2020, 12, 7395 10 of 23

Each travel time was normalized to the travel time for departure in the «free ﬂow» (departure from 1 a.m. to 4.59 a.m.)—t f ree f low. As an example, consider a part of the subindex of travel to the Kremlin (“Key route 3”) for 7.00 a.m.–7.59 a.m. departure time,

Route 3 t Route 3 Route 3 v7.00 f ree f low Q7.00 = 10 = 10 (17) Route 3 ∗ t Route 3 ∗ v f ree f low 7.00

Route 3 where t7.00 is travel time to the Kremlin for the departure interval 7.00 a.m.–7.59 a.m. For each time interval, the subindex was constructed as an arithmetic average of three sub-indexes: traveling through the Outer Moscow Ring Road (“Key route 1”), through the Third Transport Ring (“Key route 2”), and to the Kremlin and back (“Key route 3”).

2.6.4. Road Safety The probability of fatal or non-fatal injury in an accident considered in Section 2.5.3 were used as key safety characteristics for “Road safety” subindex calculation. The Road safety subindex was deﬁned as:

sa f ety Q = (Qnon f atal injury + Q f atal injury)/2 (18) −

Here, Qnon f atal injury, Q f atal injury are metrics characterizing probabilities of receiving a non-fatal or a − fatal injury in an accident, calculated as follows: Q = ln 100 P (19) f atal injury − ∗ f atal injury Qnon f atal injury = ln 100 Pnon f atal inlury (20) − − ∗ − Here, P f atal injury, Pnon f atal injury are values characterizing the probability of fatal (death) or non-fatal − injury in an accident (deﬁned in Section 2.5.3). We used -ln function to show the increase of the subindex in 1 point corresponding to the decrease of accidents probability in 2.718 times.

3. Results Integral TP Index and its subindexes were calculated for 2012–2017 for Moscow using data provided by the Moscow Traﬃc Management Center, Yandex, TomTom and described in AppendixA.

3.1. Traffic Management Efficiency: Index Structure The subindex characterizes the traffic management efficiency—how many vehicles the city can accommodate on average per day; how efficiently traffic flows are distributed in space (various roads/streets) and in time (hours). The subindex contains two key metrics:

Normalized flow speed dispersion: • - spatial, - temporal; Traffic volume. • 3.1.1. Normalized Flow Speed Dispersion The time-dependent normalized spatial speed dispersion across all Moscow streets for different hours of different years was calculated using Equation (2) and shown in Figure3a. The highest dispersion for almost all hours was observed in 2012; the dispersion gradually decreased from 2012 to its minimum value in 2016, showing trend for different routes to become more uniform in terms of vehicles speed. Sustainability 2020, 12, x FOR PEER REVIEW 10 of 23

For each time interval, the subindex was constructed as an arithmetic average of three subindexes: traveling through the Outer Moscow Ring Road (“Key route 1”), through the Third Transport Ring (“Key route 2”), and to the Kremlin and back (“Key route 3”).

Here, 𝑄 ,𝑄 are metrics characterizing probabilities of receiving a non-fatal or a fatal injury in an accident, calculated as follows:

𝑄 = −ln(100 ∗ 𝑃 ) (19)

𝑄 = −ln(100 ∗ 𝑃 ) (20)

Here, 𝑃 ,𝑃 are values characterizing the probability of fatal (death) or non- fatal injury in an accident (defined in Section 2.5.3). We used -ln function to show the increase of the subindex in 1 point corresponding to the decrease of accidents probability in 2.718 times.

3. Results Integral TP Index and its subindexes were calculated for 2012–2017 for Moscow using data provided by the Moscow Traffic Management Center, Yandex, TomTom and described in Appendix A.

3.1. Traffic Management Efficiency: Index Structure The subindex characterizes the traffic management efficiency—how many vehicles the city can accommodate on average per day; how efficiently traffic flows are distributed in space (various roads/streets) and in time (hours). The subindex contains two key metrics: • Normalized flow speed dispersion: - spatial, - temporal; • Traffic volume.

3.1.1. Normalized Flow Speed Dispersion The time-dependent normalized spatial speed dispersion across all Moscow streets for different hours of different years was calculated using Equation (2) and shown in Figure 3a. The highest dispersion for almost all hours was observed in 2012; the dispersion gradually decreased from 2012 to its minimum value in 2016, showing trend for different routes to become more uniform in terms Sustainability 2020, 12, 7395 11 of 23 of vehicles speed.

Relative spatial speed dispersion, % Relative speed dispersion, %

35% 35% 31% 30% 30% 26% 25% 23% 23% 24% 25% 21% 20% 20% 19% 19% 20% 20% 17% 16% 15% 15% 10% 10% 5% 5% 0% 0% 2012 2013 2014 2015 2016 2017 6a.m 7 a.m 7 a.m 8 a.m 9 1p.m. 2p.m. 3p.m. 4p.m. 5p.m. 6p.m. 7p.m. 8p.m. 9p.m.

10 a.m 10 a.m 11 p.m 12 Relative temporal speed dispersion, % 10p.m. 2012 2013 2014 2015 2016 2017 Relative spatial speed dispersion, % Sustainability 2020, 12, x FOR PEER REVIEW 11 of 23 (a) (b) Figure 3. Relative flow speed dispersion shown in % of the square of 24-h average speed of Figure 3. Relative ﬂow speed dispersion shown in % of the square of 24-h average speed of corresponding corresponding year. (a) Relative spatial dispersion for each hour from 6.00 a.m. to 10.00 p.m. (b) year. (a) Relative spatial dispersion for each hour from 6.00 a.m. to 10.00 p.m. (b) Relative spatial and Relative spatial and temporal speed dispersion aggregated for each year. temporal speed dispersion aggregated for each year.

FigureFigure 33bb showsshows normalizednormalized aggregatedaggregated spatialspatial andand temporaltemporal speedspeed dispersiondispersion forfor eacheach yearyear (2012–2017).(2012–2017). Since Since we we only only had had daily daily values values of of traf traﬃficc volume volume for for each each road road section section in in each each 3-week 3-week period,period, we we calculated calculated the the average average relative relative spatial spatial disp dispersionersion for for each each period period as as the the average average of of relative relative spatialspatial dispersion dispersion for for each each hour hour from from 6 a.m. to to 10 10 p.m. p.m. The The relative relative spatial spatial dispersion dispersion score score for for each each yearyear was was aggregated aggregated as as average average for for two two seasons seasons of of the the year. To To make make the the aggregation aggregation process process for for relativerelative temporal temporal dispersion dispersion similar similar to to pervious pervious one, one, we we calculated calculated the the average relative temporal dispersiondispersion for for each each period period as as average average of of relative relative spatial spatial dispersion dispersion for for each each road road (using (using all all available available citycity roads). roads). The The relative relative temporal temporal dispersion dispersion score score for for each each year year was was aggregated aggregated as as average average for for two two seasonsseasons of of the the year. year. TheThe value value of of normalized normalized temporal temporal dispersion dispersion grad graduallyually decreased decreased from from 2012 2012 to to 2016, 2016, and, and, as as in in the case of spatial dispersion, there is a slight increase in 2017. Until 2016, a decrease in the relative the case of spatial dispersion, there is a slight increase in 2017. Until 2016, a decrease in the relative dispersion of vehicle speeds was observed in Moscow: in 2012 the vehicles speed depended strongly dispersion of vehicle speeds was observed in Moscow: in 2012 the vehicles speed depended strongly on the departure time and the specific route taken, in 2016 this choice became less critical. on the departure time and the speciﬁc route taken, in 2016 this choice became less critical. The values of relative dispersions (Figure 3b) are used for calculating spatial and temporal The values of relative dispersions (Figure3b) are used for calculating spatial and temporal dispersion scores according to Equations (11) and (12). dispersion scores according to Equations (11) and (12).

3.1.2.3.1.2. Traffic Traffic Volume Volume FigureFigure 44 shows shows the the annual annual normalized normalized tra trafficffic volume volume in Moscowin Moscow (Figure (Figure4a) and 4a) itsand additive its additive score scorein Subindex in Subindex 1: “Tra 1:ffi “Trafficc management management efficiency” efficiency” (Figure (Figure4b) for 4b) spring for spring and summer. and summer.

Normalized Traffic Volume Additive Traffic Volume Score 108% 0.20 0.14 106% 0.15 104% 0.10 0.10 0.10 102% 0.05 0.05 0.05 100% 0.00 0.00 98% 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017 Spring: Additive Traffic Volume Score Spring: Normalized Traffic Volume, % Autumn: Additive Traffic Volume Score Autumn: Normalized Traffic Volume, % Total: Additive traffic volume

(a) (b)

FigureFigure 4. 4. (a()a Traffic) Traffi cvolume volume normalized normalized to tothe the minimu minimumm value value (traffic (tra ffivolumec volume for spring for spring 2012). 2012). (b) Additive(b) Additive traffic tra ffivolumec volume changing changing score score (∆𝑄 (∆Qvolume) for) for subindex subindex 1“Traffic 1 “Traffi cmanagement management efficiency”. efficiency”. ∆𝑄∆Qvolume for for bothboth seasons in 2012 (the (the first first year year of of observations) observations) is is equal equal to to 0; 0; in in 2015 2015 ∆𝑄∆Qvolume forfor springspring is is equal equal to to 0, 0, in in 2016 2016 ∆∆𝑄Qvolume for for autumn autumn is is equal equal to to 0. 0.

For simplicity of interpretation, the traffic volume values of different years in Figure 4a were normalized to the minimum value (spring 2012). It is shown that traffic volume in “Autumn” is more than in the “Spring” of the same year. The average annual traffic volume increase is 0.9% for “Spring”, 1.0% for “Autumn”. Traffic volume values are used for calculation ∆𝑄. Figure 4b shows the additive scores of traffic volume (∆𝑄) calculated according to Equation (13). ∆𝑄 is part of Subindex 1” Traffic management efficiency” (Equation (10))

3.1.3. Subindex 1 “Traffic Management Efficiency” Figure 5 shows the dynamics of the Subindex 1: “Traffic management efficiency” and its metrics (spatial and temporal speed dispersion) in 2012–2017: Sustainability 2020, 12, 7395 12 of 23

For simplicity of interpretation, the traffic volume values of different years in Figure4a were normalized to the minimum value (spring 2012). It is shown that traffic volume in “Autumn” is more than in the “Spring” of the same year. The average annual traffic volume increase is 0.9% for “Spring”, 1.0% for “Autumn”. Traffic volume values are used for calculation ∆Qvolume. Figure4b shows the additive scores of traffic volume (∆Qvolume) calculated according to Equation (13). ∆Qvolume is part of Subindex 1 “Traffic management efficiency” (Equation (10)).

3.1.3. Subindex 1 “Traffic Management Efficiency” Figure5 shows the dynamics of the Subindex 1: “Tra ffic management efficiency” and its metrics

(spatialSustainability and 2020 temporal, 12, x FOR speed PEER dispersion)REVIEW in 2012–2017: 12 of 23

Subindex 1: Traffic management efficiency 10 8.18 8.23 7.82 8.02 8.00 8 7.32 6

4 2

0 2012 2013 2014 2015 2016 2017 Temporal dispersion score Spatial dispersion score Subindex 1: Traffic management efficiency Figure 5.5. HistogramHistogram of of Subindex Subindex 1 “Tra 1 “Trafficﬃc management management eﬃciency” efficiency” in 2012–2017 in 2012–2017 and metrics and included: metrics included:spatial and spatial temporal and speedtemporal dispersion. speed dispersion.

The subindex 1 “Traffic “Traffic managementmanagement eefficiency”fficiency” is calculated using Equation (10) and contains three metrics: spatial spatial and and temporal temporal speed dispersion scores (are shown in Figure 5 5)) andand additiveadditive scorescore of tratrafficffic volume (is shown in Figure4 4b).b). TheThe scoresscores areare shownshown separatelyseparately becausebecause ofof thethe didifferencefference in scale. Figure 55 showsshows thethe growthgrowth ofof subindexsubindex 11 inin 2012–20162012–2016 andand itsits decreasedecrease inin 20172017 backback toto thethe levellevel of 2014.

3.2. Traffic Traﬃc Management Quality:Quality: Index Structure The subindex represents the average speed of road transport. For most citizens it is associated with the tratrafficﬃc quality. To determinedetermine thethe contributioncontribution of various time periods and road types to the average vehicle speeds, it was calculated for the following cases: 1. GeneralGeneral operation operation of of the the transport transport system: system: • AverageAverage 24-h speed: all all roads roads in all di directions,rections, in all weekdays and hours; • 2. DaytimeDaytime operation operation of of the the transport transport system: system: • Average speed: all roads in all directions, 11.00 a.m.–2.59 p.m. (daytime hours) for Average speed: all roads in all directions, 11.00 a.m.–2.59 p.m. (daytime hours) for weekdays • weekdays and weekends; and weekends;

3. NightNight (“free (“free flow”) ﬂow”) operatio operationn of of the transport system: • Average speed: all roads in all directions, 1.00 a.m.–4.59 p.m. (nighttime hours) for Average speed: all roads in all directions, 1.00 a.m.–4.59 p.m. (nighttime hours) for weekdays • weekdays and weekends. The “free flow” speed was used to normalize the average speed; and weekends. The “free ﬂow” speed was used to normalize the average speed; 4. Rush hours operation of the transport system: 4. Rush hours operation of the transport system: • Average speed: highways (on weekdays): oAverage Morning speed: rush hour highways (7.00 (ona.m.–9.59 weekdays): a.m.)—direction to the center, • o Evening rush hour (4.00 p.m.–7.59 p.m.)—direction from the center; • Average speed: rings (on weekdays) in all directions: o Morning rush hour (7.00 a.m.–9.59 a.m.), o Evening rush hour (4.00 p.m.–7.59 p.m.); • Average speed: roads in the center (weekdays) in all directions: o Morning rush hour (7.00 a.m.–9.59 a.m.), o Evening rush hour (4.00 p.m.–7.59 p.m.).

3.2.1. General Operation (Average 24-h Speed), Daytime Operation (Average Daytime Speed) and Night-Period Operation (“Free Flow” Speed) of the Transport System To determine the general operation of the Moscow transport system, the 24-h average speed for all roads in all directions, for all days of the week, separately for spring and autumn was calculated. The dynamics of the 24 h average speed is presented in Figure 6a. Sustainability 2020, 12, 7395 13 of 23

Morning rush hour (7.00 a.m.–9.59 a.m.)—direction to the center, ◦ Evening rush hour (4.00 p.m.–7.59 p.m.)—direction from the center; ◦ Average speed: rings (on weekdays) in all directions: • Morning rush hour (7.00 a.m.–9.59 a.m.), ◦ Evening rush hour (4.00 p.m.–7.59 p.m.); ◦ Average speed: roads in the center (weekdays) in all directions: • Morning rush hour (7.00 a.m.–9.59 a.m.), ◦ Evening rush hour (4.00 p.m.–7.59 p.m.). ◦ 3.2.1. General Operation (Average 24-h Speed), Daytime Operation (Average Daytime Speed) and Night-Period Operation (“Free Flow” Speed) of the Transport System To determine the general operation of the Moscow transport system, the 24-h average speed for all roads in all directions, for all days of the week, separately for spring and autumn was calculated. SustainabilityThe dynamics 2020, of12, thex FOR 24 PEER h average REVIEW speed is presented in Figure6a. 13 of 23

55 55 53 52 53 53 50 49 50 48 47 48 45 44 45 44 44 43 41 42 42 43 40 40 40 39 39 38 35 35 35 35 35 32 30 30 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017 Spring: Average 24-h speed, km/h Spring: daytime speed (departure time 11a.m. - Autumn: Average 24-h speed, km/h 2.59p.m.), km/h

(a) (b)

FigureFigure 6. 6. SpeedSpeed for for all all roads roads in inall all directions, directions, km/h km /(ha)( averageda) averaged over over 24 h 24 of h all of days all days of the of week, the week, (b) averaged(b) averaged over over the daytime the daytime period period (11.00 (11.00 a.m.–2.59 a.m.–2.59 p.m.) p.m.) of all of days all days of the of week. the week.

AccordingAccording to FigureFigure6 a,6a, the the maximum maximum average average 24 h24 speed h speed was was reached reached in 2015 in (532015 km (53/h forkm/h spring for springand 48 and km/ h48 for km/h autumn). for autumn). The minimum The minimum was reached was re inached 2017, in returning 2017, returning to the values to the of values 2012. Theof 2012. 24-h Thespeeds 24-h are speeds compared are compared with the with daytime the daytime (11.00 a.m.–2.59 (11.00 a.m.–2.59 p.m. on p.m. all days on all of days the week)of the week) average average speed speedfor all for Moscow all Moscow roads (Figureroads (Figure6b). The 6b). comparison The comparis of Figureon of6 a,bFigure shows 6a,b that show dynamicss that dynamics of the daytime of the daytimespeed is speed relatively is relatively close to theclose dynamics to the dynamics for average for 24-haverage speed. 24-h speed. FigureFigure 77a,ba,b showsshows thethe annualannual decreasedecrease ofof “free“free flow”flow” speedspeed (departure(departure timetime fromfrom 1.001.00 a.m.a.m. to 5.005.00 a.m.). a.m.). The The decrease decrease could could be be explained explained by by th thee annual annual increase increase in in the the number number of of control control speed speed cameras,cameras, which which led led to to higher higher compliance compliance to to the the speed speed limits limits by by drivers. drivers. According According to to Equation Equation (15) (15) “free flow”flow” speed (Figure(Figure7 7a)a) is is used used for for normalization normalization of of all all speed speed values values included included in in the the subindex subindex 2 2“Tra “Trafficffic management management quality”. quality”. Figure Figure7b with 7b with dynamics dynamics of “free of flow”“free speedflow” forspeed di ff forerent different road types road is typesshown is forshown comparison for comparison of its components. of its components.

80 80 75 75 76 74 73 70 72 70 71 70 67 65 63 65 65 60 61 60 60 59 58 58 58 58 57 55 56 56 55 55 57 56 56 56 55 54 54 52 53 52 53 50 50 51 49 47 45 45 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017 Spring: free flow speed (departure time 1p.m - 4.59p.m), km/h Highways_"free flow" Ring roads_"free flow" City center_free flow All roads_free flow Autumn: free flow speed (departure time 1p.m - 4.59p.m), km/h

(a) (b)

Figure 7. “Free flow” speed (departure time from 1 a.m. to 4.59 a.m.) (a) for all road types in all directions, separately for spring and autumn, km/h, (b) for various road types, km/h.

3.2.2. Rush Hours Operation of the Transport System To determine the efficiency of the transport system for rush hours, various types of roads were separately considered—highways, rings, city center roads (Figure 8): Sustainability 2020, 12, x FOR PEER REVIEW 13 of 23

(a) (b)

Figure 6. Speed for all roads in all directions, km/h (a) averaged over 24 h of all days of the week, (b) averaged over the daytime period (11.00 a.m.–2.59 p.m.) of all days of the week.

According to Figure 6a, the maximum average 24 h speed was reached in 2015 (53 km/h for spring and 48 km/h for autumn). The minimum was reached in 2017, returning to the values of 2012. The 24-h speeds are compared with the daytime (11.00 a.m.–2.59 p.m. on all days of the week) average speed for all Moscow roads (Figure 6b). The comparison of Figure 6a,b shows that dynamics of the daytime speed is relatively close to the dynamics for average 24-h speed. Figure 7a,b shows the annual decrease of “free flow” speed (departure time from 1.00 a.m. to 5.00 a.m.). The decrease could be explained by the annual increase in the number of control speed cameras, which led to higher compliance to the speed limits by drivers. According to Equation (15) “free flow” speed (Figure 7a) is used for normalization of all speed values included in the subindex 2 “Traffic management quality”. Figure 7b with dynamics of “free flow” speed for different road Sustainability 2020, 12, 7395 14 of 23 types is shown for comparison of its components.

(a) (b)

FigureFigure 7. 7.“Free “Free ﬂow” flow” speed speed (departure (departure time time from from 1 1 a.m. a.m. to to 4.59 4.59 a.m.) a.m.) (a(a)) forfor all all road road types types in in all all directions,directions, separately separately for for spring spring and and autumn, autumn, km km/h,/h, (b ()b for) for various various road road types, types, km km/h./h.

3.2.2.3.2.2. Rush Rush Hours Hours Operation Operation of of the the Transport Transport System System ToTo determine determine the the e ﬃefficiencyciency of of the the transport transport system system for for rush rush hours, hours, various various types types of of roads roads were were separatelySustainabilityseparately considered—highways,2020 considered—highways,, 12, x FOR PEER REVIEW rings, rings, city city center center roads roads (Figure (Figure8): 8): 14 of 23

50 45 40 35 Spring: Highways_morning 30 Spring: Highways_evening 30 30 3028 3028 272627 26 27 25 2425 2625 242223 23 24 Autumn: Highways_morning 20 19 20 21 15 Autumn: Highways_evening 10 2012 2013 2014 2015 2016 2017

(a)

50 50 47 49 49 45 45 45 44 45 40 41 40 40 41 39 40 39 38 39 39 37 36 35 37 36 36 35 36 35 33 34 32 30 31 34 32 31 30 30 30 27 28 29 25 25 24 24 23 20 21 22 22 20 18 19 15 16 17 16 16 15 13 10 10 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017 Spring: Ring roads_morning Spring: City center_morning Spring: Ring roads_evening Spring: City center_evening Autumn: City center_morning Autumn: Ring roads_morning Autumn: City center_evening Autumn: Ring roads_evening

(b) (c)

Figure 8. Average speed during morning andand evening rush hours, kmkm/h:/h: ( a) highway, ((b)) ring,ring, ((c)) city centercenter roads.roads.

The positivepositive dynamics dynamics is shownis shown for highwaysfor highways Figure Figure8a. For 8a. example, For example, average speedaverage on highwaysspeed on duringhighways the during morning the rush morning hours rush in autumn hours in increased autumn every increased year every and reached year and the reached value of the 26 value km/h inof 2017,26 km/h this in is 2017, 37% higherthis is 37% than higher in 2012. than For in rings 2012. (Figure For rings8b)and (Figure central 8b) streetsand central (Figure streets8c) the (Figure trend is8c) inverse:the trend the is valuesinverse: in the 2017 values are lower in 2017 than are ones lower in 2012. than The ones possible in 2012. causes The forpossible such dynamics causes for will such be considereddynamics will in the be Discussionconsidered sectionin the Discussion of this study. section of this study.

3.2.3. Subindex 2 “Traffic Management Quality” The dynamics of the subindex “Traffic management quality” and its metrics are presented in Figure 9:

Subindex 2. Traffic Management Quality 10 7.16 7.18 6.62 6.61 8 6.31 6 5.87 4 2 0 2012 2013 2014 2015 2016 2017 Average 24-h speed score Daytime speed score Rush hour speed score Subindex 2. Traffic Management Quality

Figure 9. Histogram of Subindex 2: “Traffic management quality” and included metrics in 2012–2017. The daytime and average 24-h speed scores for each year were calculated as average of corresponding score for “spring” and “autumn” periods of each year, based on speeds shown on Figure 6. The rush hour scores were calculated separately for different road types and seasons, based on speeds shown of Figure 8. The average rush hour speed score for each year is the average of scores for different road types and seasons of the year. Sustainability 2020, 12, x FOR PEER REVIEW 14 of 23

(a)

(b) (c)

Figure 8. Average speed during morning and evening rush hours, km/h: (a) highway, (b) ring, (c) city center roads.

The positive dynamics is shown for highways Figure 8a. For example, average speed on highways during the morning rush hours in autumn increased every year and reached the value of 26 km/h in 2017, this is 37% higher than in 2012. For rings (Figure 8b) and central streets (Figure 8c) Sustainabilitythe trend is2020 inverse:, 12, 7395 the values in 2017 are lower than ones in 2012. The possible causes for15 such of 23 dynamics will be considered in the Discussion section of this study.

3.2.3. Subindex 2 “Traffic “Traﬃc ManagementManagement Quality”Quality” The dynamics of the subindex “Tra“Trafficﬃc managementmanagement quality” and its metrics are presented in Figure9 9::

Figure 9. Histogram of Subindex Subindex 2: 2: “Traffic “Traffic manageme managementnt quality” and included included metrics metrics in in 2012–2017. 2012–2017. The daytime and average 24-h speed scores for each ye yearar were calculated as average of corresponding score for “spring” and “autumn” periods of each year,year, basedbased onon speedsspeeds shownshown onon FigureFigure6 6.. TheThe rushrush hour scores werewere calculatedcalculated separatelyseparately for for di differenfferent roadt road types types and and seasons, seasons, based based on on speeds speeds shown shown of Figureof Figure8. The8. The average average rush rush hour hour speed speed score score for for each each year year is is the the average average of of scores scores for for di differentfferent road types and seasons ofof thethe year.year.

The subindex 2 “Traﬃc management quality” was calculated using Equation (14). It contains three metrics (Equation (15)): scores for average speed values (24-h, daytime and rush hours) and all TM Quality of them is shown on Figure9. As we can see, the values of subindex 2 for 2015 (Q 2015) and TM Quality 2016 (Q 2016) are almost equal and have maximum values for all years of observations. TM Quality TM Quality TM Quality The Q 2017 is almost the same as Q 2014 and 8% less than Q 2016 or TM Quality Q 2015. It also should be mentioned that the absolute values and dynamics of daytime speed score is close to subindex 2 values and dynamics. The values of subindex 2 shows the speed loss because of traﬃc congestion. For example, it is shown that in 2012 the reduction was about 41% and in 2017—only 28%.

3.3. Transit Efficiency: Index Structure Transit efficiency characterizes travel time for the key routes within the key departure intervals. The key routes in Moscow were selected as follows: from North to South, and from West to East for different travel options (via the Outer Moscow Ring Road, via the Third Transport Ring) and from the Kremlin to the Outer Moscow Ring Road (and back). The key departure intervals are: “7 a.m.”, “12 p.m.”, “5 p.m.”. “7 a.m.”: 7.00–7.59 a.m., “12 p.m.”: 12.00–12.59 p.m., etc. Time periods “7 a.m.” and “5 p.m.” were selected to represent rush hours, the period “12 p.m.” is within the daytime period. The longer it takes to travel along the key routes in key departure time intervals, the more time citizens lose in transit daily. Detailed structure of Subindex 3: “Transit Efficiency” is presented as follows (Figure 10):

1. Travel time via the Moscow Outer Ring Road—“Key Route 1” (Figure 10a):

from North to South, • from West to East; • 2. Travel time via the Third Transport Ring—“Key Route 2” (Figure 10b):

by the key highways (Leninskiy Prospekt, Kutuzovskiy Prospekt, Prospekt Mira, • Volgogradskiy Prospekt), Sustainability 2020, 12, x FOR PEER REVIEW 15 of 23

The subindex 2 “Traffic management quality” was calculated using Equation (14). It contains three metrics (Equation (15)): scores for average speed values (24-h, daytime and rush hours) and all of them is shown on Figure 9. As we can see, the values of subindex 2 for 2015 (𝑄 ) and 2016 (𝑄 ) are almost equal and have maximum values for all years of observations. The 𝑄 is almost the same as 𝑄 and 8% less than 𝑄 or 𝑄 . It also should be mentioned that the absolute values and dynamics of daytime speed score is close to subindex 2 values and dynamics. The values of subindex 2 shows the speed loss because of traffic congestion. For example, it is shown that in 2012 the reduction was about 41% and in 2017— only 28%.

3.3. Transit Efficiency: Index Structure Transit efficiency characterizes travel time for the key routes within the key departure intervals. The key routes in Moscow were selected as follows: from North to South, and from West to East for different travel options (via the Outer Moscow Ring Road, via the Third Transport Ring) and from the Kremlin to the Outer Moscow Ring Road (and back). The key departure intervals are: “7 a.m.”, “12 p.m.”, “5 p.m.”. “7 a.m.”: 7.00–7.59 a.m., “12 p.m.”: 12.00–12.59 p.m., etc. Time periods “7 a.m.” and “5 p.m.” were selected to represent rush hours, the period “12 p.m.” is within the daytime period. The longer it takes to travel along the key routes in key departure time intervals, the more time citizens lose in transit daily. Detailed structure of Subindex 3: “Transit Efficiency” is presented as follows (Figure 10): 1. Travel time via the Moscow Outer Ring Road—“Key Route 1” (Figure 10a): • from North to South, • from West to East;

Sustainability2. Travel2020 time, 12 via, 7395 the Third Transport Ring—“Key Route 2” (Figure 10b): 16 of 23 • by the key highways (Leninskiy Prospekt, Kutuzovskiy Prospekt, Prospekt Mira, Volgogradskiyfrom North to South,Prospekt), •• fromfrom North West to to East; South, •• from West to East; 3. To the Kremlin: travel time from the Outer Road and back—“Key Route 3” (Figure 10c) 3. To the Kremlin: travel time from the Outer Road and back—“Key Route 3” (Figure 10c) by highways: Rublevskoye, Mozhayskoye, Leningradskoye Highway, Leninskiy Prospekt. •• by highways: Rublevskoye, Mozhayskoye, Leningradskoye Highway, Leninskiy Prospekt.

(a) (b) (с)

Figure 10.10.Schematic Schematic representation representation of Moscowof Moscow roads roads for the for “Transit the E“Transitﬃciency” Efficiency” subindex calculation. subindex Thecalculation. following The roads following are marked roads in are red: marked (a) the Outerin red: Ring (a) the Road—“Key Outer Ring Route Road—“Key 1”; (b) the Route Third 1”; Transport (b) the RingThird and Transport key highways Ring and (Leninskiy key highways Prospekt, (Leninskiy Kutuzovskiy Prospekt, Prospekt, Kutuzovskiy Prospekt Prospekt, Mira, Prospekt Volgogradskiy Mira, Prospekt)—“KeyVolgogradskiy Prospekt)—“Key Route 2”; (c) To the Route Kremlin 2”; by (c Rublevskoye,) To the Kremlin Mozhayskoye, by Rubl Leningradskoyeevskoye, Mozhayskoye, Highway, LeninskiyLeningradskoye Prospekt Highway, and back—“Key Leninskiy Route Prospekt 3”. and back—“Key Route 3”.

There were used data for roads and road sections.sections. The data of road sections were implemented for subindexsubindex 3 3 “Transit “Transit eﬃ efficiency”ciency” calculation calculation when when route route contained contained only part only of part the road of the (for road example, (for “Leninskiyexample, “Leninskiy Prospect—B. Prospect—B. Yakimanka”, Yakimanka”, “the Third “the Transport Third Ring—SeraﬁmovichTransport Ring—Serafimovich St.”, etc.). St.”, etc.).

3.3.1. Dynamics of Characteristic Travel Times for the Key Routes For travel in the directions N-S and W-E by the Outer Ring Road—“The Key Route 1” (Figure 11a), the “fastest” year was 2015 and then, in 2016 and 2017, the travel time increased. Similar dynamics were observed for the travel time of N-S and W-E via the Third Transport Ring Sustainability 2020, 12, x FOR PEER REVIEW 16 of 23 and key highways (Figure 11b). The travel time from the Kremlin to the Moscow Outer Ring Road 3.3.1.(Figure Dynamics 11c) varies of Characteristic less than for the Travel previous Times routes. for the The Key fastest Routes year was 2015. In 2017, a trip to the Kremlin and back took about the same time as in 2012. The fastest transit way through Moscow is by the longestFor travel route—key in the directions route 1 (Figures N-S and 10 andW-E 11 by). the This Outer rout theRing only Road—“The one without Key tra ﬃRoutec lights 1” and(Figure has 11a),the highest the “fastest” speed year limit. was 2015 and then, in 2016 and 2017, the travel time increased.

100 100 90 92 87 90 90 89 88 80 78 76 79 78 77 80 80 81 81 73 74 72 72 75 78 75 70 67 68 70 72 72 60 65 64 58 60 61 62 54 56 56 56 58 60 50 50 45 44 50 48 49 49 49 40 42 46 46 40 39 39 40 30 30 20 20 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017 7a.m. 12p.m. 5p.m. free flow 7a.m. 12p.m. 5p.m. free flow

(a) (b)

Figure 11. Cont.

100 99 9798 95 93 9597 90 93 91 89 8688 80 81 82 80 76 75 70 74 60 62 60 58 57 56 57 50 40

30 20 2012 2013 2014 2015 2016 2017 7a.m. 12p.m. 5p.m. "free flow"

(c)

Figure 11. The average travel time for various routes and departure intervals, min. (a) “The Key Route 1”: from North to South and from West to East via the Moscow Outer Ring Road (b) “The Key Route 2”: from North to South and from West to East via the Third Transport Ring and Leninskiy Prospekt, Kutuzovskiy Prospekt, Prospekt Mira, Volgogradskiy Prospekt (c) “The Key Route 3”: from the Kremlin to the Outer Moscow Ring Road through Rublevskoye, Mozhayskoye, Leningradskoye Highway, Leninskiy Prospekt and back.

Similar dynamics were observed for the travel time of N-S and W-E via the Third Transport Ring and key highways (Figure 11b). The travel time from the Kremlin to the Moscow Outer Ring Road (Figure 11c) varies less than for the previous routes. The fastest year was 2015. In 2017, a trip to the Kremlin and back took about the same time as in 2012. The fastest transit way through Moscow is by the longest route—key route 1 (Figures 10 and 11). This rout the only one without traffic lights and has the highest speed limit.

3.3.2. Subindex 3 “Transit Efficiency” The dynamics of the “Transit Efficiency” subindex is presented in Figure 12: Sustainability 2020, 12, x FOR PEER REVIEW 16 of 23

100 99 9798 95 93 9597 90 93 91 89 8688 80 81 82 80 76 75 70 74 60 62 60 58 57 56 57 50 40

30 20 2012 2013 2014 2015 2016 2017 7a.m. 12p.m. 5p.m. "free flow"

(c)

FigureFigure 11. 11. TheThe average average travel travel time time for forvarious various routes routes and anddeparture departure intervals, intervals, min. ( min.a) “The (a )Key “The Route Key 1”:Route from 1”: North from to North South to and South from and West from to West East tovia East the viaMoscow the Moscow Outer Ring Outer Road Ring (b Road) “The (b Key) “The Route Key 2”:Route from 2”: North from to North South toand South from and West from to East West vi toa the East Third via theTransport Third TransportRing and Leninskiy Ring and Prospekt, Leninskiy KutuzovskiyProspekt, Kutuzovskiy Prospekt, Prospekt,Prospekt ProspektMira, Volgogradskiy Mira, Volgogradskiy Prospekt Prospekt (c) “The (c )Key “The Route Key Route 3”: from 3”: fromthe Kremlinthe Kremlin to the to theOuter Outer Moscow Moscow Ring Ring Road Road throug throughh Rublevskoye, Rublevskoye, Mozhaysk Mozhayskoye,oye, Leningradskoye Leningradskoye Highway,Highway, Leninskiy Leninskiy Prospekt and back.

3.3.2.Similar Subindex dynamics 3 “Transit were E ﬃobservedciency” for the travel time of N-S and W-E via the Third Transport Ring and key highways (Figure 11b). The travel time from the Kremlin to the Moscow Outer Ring Road SustainabilityThe dynamics 2020, 12, x FOR of the PEER “Transit REVIEW E ﬃciency” subindex is presented in Figure 12: 17 of 23 (Figure 11c) varies less than for the previous routes. The fastest year was 2015. In 2017, a trip to the Kremlin and back took about the same time as in 2012. The fastest transit way through Moscow is by the longest route—key route 1 (FiguresSubindex 10 and 3. Transit11). This Efficiencyrout the only one without traffic lights and has the10 highest speed limit.

3.3.2. Subindex8 3 “Transit Efficiency” 6.36 6.53 6.50 6.31 6.30 The dynamics6.17 of the “Transit Efficiency” subindex is presented in Figure 12: 6

2 0 2012 2013 2014 2015 2016 2017 “The Key Route 3” score “The Key Route 1” score “The Key Route 2” score Subindex 3.Transit Efficiency Index

Figure 12. HistogramHistogram of of Subindex Subindex 3: 3:“Transit “Transit efficiency eﬃciency”” and and metrics metrics included included in inthe the Subindex Subindex in 2012–2017.in 2012–2017.

The subindex 3 values were calculated using EquationsEquations (16) and (17)—Section 2.6.32.6.3.. The The scores for different diﬀerent routes are slightly changed for 2012–2017.2012–2017. The subindex 3 has it itss maximum value in 2014 2014 and showed nearly the same low values in 2013, 2016 and 2017.

3.4. Road Safety: Index Structure The subindex is based on two main metrics: probability of fatal or non-fatal injury in a road accident.

3.4.1. The Probability of Injury or Death in an Accident In this study, road safety was assessed as the probability of fatal or non-fatal injury in an accident (per day) for “spring” and “autumn” periods in 2012–2017 (Figure 13). Since the data on fatal and non-fatal accidents in 2012 were not available, the numbers of accidents in 2012 were approximated using lineal functions.

Probability of Probability of fatal injury non-fatal injury 0.0002% 0.003% 0.0002% 0.002% 0.002% 0.0001% 0.001% 0.0001% 0.001% 0.000% 0.0000% 2012 2013 2014 2015 2016 2017 2012 2013 2014 2015 2016 2017 Probability of non-fatal injury for spring,% Probability of fatal injury for spring,% Probability of non-fatal injury for autumn,% Probability of fatal injury for autumn,%

(a) (b)

Figure 13. For “Spring” and “Autumn” period 2012–2017 (a) Probability of non-fatal injury in an accident, % (b) Probability of fatal injury an accident, %.

It is shown that the most “dangerous” year was 2012, and 2017 can be called the “safest” during the entire observation period. The probability of a fatal injury in an accident is one order of magnitude lower than non-fatal and it shows steeper decrease. Sustainability 2020, 12, x FOR PEER REVIEW 17 of 23

Subindex 3. Transit Efficiency 10

8 6.17 6.36 6.53 6.50 6.31 6.30 6

2 0 2012 2013 2014 2015 2016 2017 “The Key Route 3” score “The Key Route 1” score “The Key Route 2” score Subindex 3.Transit Efficiency Index

Figure 12. Histogram of Subindex 3: “Transit efficiency” and metrics included in the Subindex in 2012–2017.

The subindex 3 values were calculated using Equations (16) and (17)—Section 2.6.3. The scores for different routes are slightly changed for 2012–2017. The subindex 3 has its maximum value in 2014 and showed nearly the same low values in 2013, 2016 and 2017. Sustainability 2020, 12, 7395 18 of 23 3.4. Road Safety: Index Structure 3.4. RoadThe Safety: subindex Index is Structure based on two main metrics: probability of fatal or non-fatal injury in a road accident.The subindex is based on two main metrics: probability of fatal or non-fatal injury in a road accident.

3.4.1.3.4.1. The The Probability Probability of of Injury Injury or or Death Death in in an an Accident Accident InIn this this study, study, road road safety safety was was assessed assessed as as the the probability probability of of fatal fatal or or non-fatal non-fatal injury injury in in an an accident accident (per(per day) day) for for “spring” “spring” and and “autumn” “autumn” periods periods in in 2012–2017 2012–2017 (Figure (Figure 13 13).). Since Since the the data data on on fatal fatal and and non-fatalnon-fatal accidents accidents in in 2012 2012 were were not not available, available, the the numbers numbers of of accidents accidents in in 2012 2012 were were approximated approximated usingusing lineal lineal functions. functions.

(a) (b)

FigureFigure 13. 13.For For “Spring” “Spring” and and “Autumn” “Autumn” period period 2012–2017 2012–2017 ( a()a) Probability Probability of of non-fatal non-fatal injury injury in in an an accident,accident, % % (b ()b Probability) Probability of of fatal fatal injury injury an an accident, accident, %. %.

ItIt is is shown shown that that the the most most “dangerous” “dangerous” year year was was 2012, 2012, and and 2017 2017 can can be be called called the the “safest” “safest” during during thethe entire entire observation observation period. period. The The probability probability of of a a fatal fata injuryl injury in in an an accident accident is is one one order order of of magnitude magnitude Sustainabilitylowerlower than than 2020 non-fatal non-fatal, 12, x FOR and and PEER it it shows REVIEWshows steeper steeper decrease. decrease. 18 of 23

3.4.2. Subindex Subindex 4 “Road Safety” The trend described above is confirmed conﬁrmed by the gr graphaph of of the the subindex subindex “Road “Road safety” safety” (Figure (Figure 14).14). Dashed lines show that the data on fatal and non-fatal accidents in 2012 werewere approximated.approximated.

Subindex 4. Road Safety 10 7.96 7.80 7.92 7.38 7.47 7.59 8 6 4 2 0 2012 2013 2014 2015 2016 2017 Non-fatal injury score Fatal injury score Subindex 4. Road Safety

Figure 14. Histogram of Subindex 4: “Road “Road sa safety”fety” and included metrics in 2012–2017.

The slight decrease of non-fatal ac accidentscidents prevents to tiny increase in non-fatal score. The growth of subindex is connected with increaseincrease of fatal injury score.

3.5. Integral Index of Traffic Planning Applied to Moscow City Transportation System Integral TP Index for Moscow is shown in Figure 15:

Integral Index of Traffic Planning 10 Subindex 1. Traffic Management 7,19 7,41 7,41 7,22 Efficiency 8 6,69 6,99 Subindex 2. Traffic Management 6 Quality Subindex 3. Transit Efficiency 4 Subindex 4. Road Safety 2

0 Integral Index of Traffic Planning 2012 2013 2014 2015 2016 2017

Figure 15. Histogram of the Integral TP Index and its subindexes in 2012–2017.

The Integral TP Index was calculated for years 2012–2017. It clearly illustrates the dynamics of subindexes presented earlier and allows us to conclude about the transportation planning in Moscow during the 2012–2017 period: in 2012–2015 the overall TP Index value showed increase, in 2016 it remained at the level of 2015, and in 2017 it decreased almost to the level of 2014. It is also well seen that Moscow authorities should take attention to traffic management efficiency and traffic management quality, because this subindexes decreased in 2017 and led to decrease of Integral Index. The amplitude of changes of Integral index values is only about 12%. The possible reasons of the Integral index dynamics and its small changes are presented in the Section 4. “Discussion”.

4. Discussion The main goal of the work was to develop a methodology for tracking traffic planning based on the most accessible for collection primary data for Moscow. The list of such data is presented in section “Materials and Methods”. Therefore, the TP Index uses frequently cited transport and Sustainability 2020, 12, x FOR PEER REVIEW 18 of 23

3.4.2. Subindex 4 “Road Safety” The trend described above is confirmed by the graph of the subindex “Road safety” (Figure 14). Dashed lines show that the data on fatal and non-fatal accidents in 2012 were approximated.

Subindex 4. Road Safety 10 7.96 7.80 7.92 7.38 7.47 7.59 8 6 4 2 0 2012 2013 2014 2015 2016 2017 Non-fatal injury score Fatal injury score Subindex 4. Road Safety

Figure 14. Histogram of Subindex 4: “Road safety” and included metrics in 2012–2017.

SustainabilityThe slight2020, decrease12, 7395 of non-fatal accidents prevents to tiny increase in non-fatal score. The growth19 of 23 of subindex is connected with increase of fatal injury score.

3.5. Integral Integral Index Index of of Traffic Traﬃc Planning AppliedApplied to Moscow CityCity TransportationTransportation SystemSystem IntegralIntegral TP Index for Moscow is shown in Figure 1515::

Integral Index of Traffic Planning 10 Subindex 1. Traffic Management 7.19 7.41 7.41 7.22 Efficiency 8 6.69 6.99 Subindex 2. Traffic Management 6 Quality Subindex 3. Transit Efficiency 4 Subindex 4. Road Safety 2

0 Integral Index of Traffic Planning 2012 2013 2014 2015 2016 2017

Figure 15. HistogramHistogram of of the the Integral Integral TP TP In Indexdex and and its its subindexes subindexes in 2012–2017.

The Integral TP Index was calculated for years 2012–2017.2012–2017. It It clearly illustrates the dynamics of subindexessubindexes presented earlier and allows us us to to conc concludelude about about the the transportation transportation planning planning in in Moscow Moscow during the 2012–2017 period: in in 2012–2015 2012–2015 the the overa overallll TP TP Index Index value value showed showed increase, increase, in in 2016 2016 it remainedremained at the level of 2015, and in 2017 it decreased almost to the level of 2014. It It is is also also well well seen thatthat MoscowMoscow authorities authorities should should take take attention attention to traffi tocmanagement traffic management efficiency andefficiency traffic managementand traffic managementquality, because quality, this subindexes because this decreased subindexes in 2017decreased and led in to2017 decrease and led of to Integral decrease Index. of Integral Index. The amplitude of changes of Integral index values is only about 12%. The The possible possible reasons reasons of the the IntegralIntegral index dynamics and its small changes are presented in the Section4 4.. “Discussion”. “Discussion”.

4. Discussion Discussion The main main goal goal of of the the work work was was to to develop develop a meth a methodologyodology for for tracking tracking traffic traﬃ planningc planning based based on theon themost most accessible accessible for forcollection collection primary primary data data for for Moscow. Moscow. The The list list of of such such data data is is presented presented in sectionsection “Materials“Materials and and Methods”. Methods”. Therefore, Therefore, the theTP IndexTP Index uses frequentlyuses frequently cited transport cited transport and mobility and indicators, extended by special indicators, available for calculation on accessible primary data.

4.1. Ranges of Subindexes and Its Combination to Create the Integral Index The final stage in creating the Integral Traffic Planning Index involves combining the four subindexes with the same weight. It is considered reasonable that all subindexes included in the Index are equally significant for assessing traffic planning in metropolis like Moscow. At the same time, the metrics included in the subindexes are based on data for Moscow and take into account the uniform collection of data and the absence of sharp fluctuations. For constructing a more general index and expanding it to other cities, Defi method could be used to estimate the weights of subindexes (Dimitrijevićet al. [29]). Practically the range of subindexes values cannot be considered only according to formal mathematical point of view, since, in reality, the parameters included in metrics are partially related and specific for transportation complex. The best criterion is the result of their implementation to specific data. For example, according to Equation (13), relative traffic volume changes of the year compared to the previous one, can vary in a range [ 20,20]. But for Moscow (as for many other metropolises) − transportation system is close to the saturation and relative traffic volume changes would not change traffic management efficiency subindex significantly (to exceed the value of 10). Sustainability 2020, 12, 7395 20 of 23

4.2. Practical Range of the Integral Index changes According to Figure 15, the values of Integral Index did not change much between 2014 and 2017. This indicates the balance of the subindexes and metrics included in the index, while the metrics themselves changed more significant during the observation period (Figure9). It should also be mentioned that the transport system of a megalopolis is a very static, the changes of integral effects accumulate slowly. For example, increase in average traffic volume by 1% per year is small enough in relative integral values, but, in reality, it has a strong impact on the system, especially in bottlenecks. City program called “My Street” was implemented in Moscow in 2015–2018 [30]. As part of this program, the construction of interchanges, the allocation of bus lanes, the expansion of the roadway, etc., were carried out, this caused temporary transportation difficulties because of street renovations. The 2012 year was the last one before the program implementation. As we can see, the Integral TP Index for this year has the smallest value for the observation period. The small changes in values of TP Index in 2012–2017 (especially in 2013–2017) also suggest that the urban network in Moscow is fairly well balanced and, due to the redistribution of flows, coped with the influence of street repairs. Thus, the “equilibrium state” for the Moscow in 2012–2017 was demonstrated. Tracking of long-term changes of TP Index and the causes of changes will become a subject for future research.

4.3. The Integral Index Generalization and Its Contribution to City Decision-Making Process The Index has two main applications for city decision making. The first application is subindexes tracking. Each subindex (group of indicators) tracks changes in the field connected with traffic planning and answers the following main questions (the answers to these questions for Moscow are presented in Figures matched below): Subindex 1. Traffic management efficiency: How many vehicles city accommodates on average? (Figure4a) How e fficiently are traffic flows distributed in space (city streets) and in time (hours)? (Figure3a,b) Subindex 2. Traffic management quality: What is the average car travel speed one can expect depending on departure time and road type? (Figures6–8) What is the average speed reduction caused by traffic congestion? (Figures7a and8) Subindex 3. Transit efficiency: How long will it take to travel through the city by different key routes? Which key route is the fastest? (Figure 11) Subindex 4. Road safety: What is the daily probability of being died in a road accident? (Figure 13b) What is the daily probability of being injured in a road accident? (Figure 13a) Tracking changes in the subindexes allows the questions above to be re-answered for different years and understand connected changes in city traffic planning. The second application is the Integral TP Index tracking. The TP Index is an instrument for useful and simple assessing of traffic planning, it allows to obtain integral feedback on the qualitative and quantitative dynamics of changes in city traffic planning for the last years based on the available historical data on vehicle speeds, traffic volumes, number of accidents, etc. The purpose of the research was to integrate well-known metrices and new ideas relating to transportation system and provide megapolises with new conception of transportation index. The example of integration of the concept to real transportation complex was realized for Moscow. The implementation of the proposed TP Index to another megapolises should be based on direct measurements from the road system. The basic data for TP Index construction are: daily traffic volumes, hourly average speeds for basic city roads and rate of road traffic accidents for basic roads (the data structure is described in AppendixA) for at least three weeks in approximately congested and uncongested period (for Moscow it was mid-October and mid-May, respectively (Section 2.2)). The restrictions mentioned above in “Subindexes ranges” and “Real range of Integral Index changes” should also be taken into account for expending TP Index to other cities. There is a potential problem in comparison of Integral TP Index values for individual cities: data for individual cities are hard to Sustainability 2020, 12, 7395 21 of 23 come by, and, even though every city has data, the data are not always comparable. This problem is stated by the Bureau of Transportation Statistics in the U.S (Casey and Norwood [13]).

5. Conclusions The Integral Index of Traffic Planning (TP Index) provides a basis for measuring indicators which are the most important for traffic planning in megapolis based on frequently used and cited transport and mobility indicators, supplemented with more specific measures, and aggregated in four groups: traffic management efficiency, traffic management quality, transit efficiency and road safety. In its present form the Integral TP Index provide a guide for trends in traffic planning and infrastructure evaluation in megapolis into the future. Although the primary data for analysis was limited due to technical and resource constraints, the results are quite reasonable and consistent with real transportation situation in Moscow. The TP Index will provide a basis for future expansion and improvement on the measurement of traffic planning in any metropolis with radial-ring street structure.

Author Contributions: Conceptualization, A.G. and A.S.; methodology, T.P. and A.G.; data obtaining, A.S.; data analysis, visualization and validation, T.P.; writing—original draft preparation, T.P.; writing—review and editing, T.P. and A.G. The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by Moscow Department for Transport and Road Infrastructure Development (agreement number 101-DT-RDTI-C) and as part of research program of MSU Center for Storage and Analysis of Big Data. Acknowledgments: The authors thank Yandex, TomTom and Moscow Traffic Management Center for providing data for analysis and discussion of research results. Conflicts of Interest: The authors declare no conflict of interest.

Appendix A Since the data provided by the Moscow Traﬃc Management Center, Yandex and TomTom are conﬁdential and cannot be published, we present a description of the data format.

Appendix A.1 Yandex Yandex provided average travel times for the main routes in 2012–2017 based on the statistics gather through mobile applications Yandex.Maps and Yandex.Navigator. These data were used to calculate the main indicators of transportation planning eﬃciency: average transit time, average speed, speed dispersion and other. To test some of the statistical hypotheses, partial data for the “Spring” period of 2018 was used. Data were provided in tables with the following columns:

Name—name of the street object (for example, The Third Transport Ring); • Part1—part of the street object (for example, “Kaluzhskoye highway”—“Novorizhskoye • highway”), partially available across the dataset; Direction—direction of movement (for example, for rings—“outer part” and “inner part”, for • highways—“to the city center”—“from the city center”); Date—date (day.month.year) attribute of the data; • Hour—one-hour period (for example, 8:00, i.e., 8:00–8.59) attribute of the data; • Second—typical travel time in seconds. • Links to speciﬁc roads and road sections on Yandex.Maps were also provided, allowing the length of each section to BE determined.

Appendix A.2 Moscow Traffic Management Center The Moscow Traffic Management Center provided data on the average vehicles speed on selected streets, traffic volumes, accidents rate (for 2012–2017, for some streets and data types—for 2015–2017). Sustainability 2020, 12, 7395 22 of 23

Traffic volume and average speed values were collected from more than 6700 detectors and 2000 cameras, and further processed in the situation center. Traffic volume data was used to calculate the subindexes “Traffic Management Efficiency” (Sections 2.5.1, 2.5.2 and 2.6.1), “Traffic Management Quality” (Sections 2.5.1 and 2.6.2) and “Road safety” (Sections 2.5.3 and 2.6.4), data on accidents in 2013–2017—to calculate the “Road Safety” metrics. Data were presented in separate tables. Average traffic volume and average speed data were available in subsets of daily averages for some roads and sections, and larger periods (e.g., 28 April 2014–18 May 2014) for others. There were roads and road sections for which no data on average traffic volume were available.

Appendix A.3 TomTom This source provided the smallest dataset, covering only the average speeds for some road sections in 2015–2017. Due to the small sample, TomTom data were only used to verify and supplement data from other sources.

References

1. Traffic Jams in Moscow: 2013–2017. (In Russian). Available online: https://yandex.ru/company/researches/ 2017/moscow_traffic_2017 (accessed on 14 July 2020). 2. Litman, T. Well Measured: Developing Indicators for Sustainable and Livable Transport Planning. Victoria Transport Policy Institute: Victoria, BC, Canada, 2019. Available online: https://www.vtpi.org/wellmeas.pdf (accessed on 14 July 2020). 3. United States Department of Transportation/Transportation Services Indexes. Available online: https: //www.transtats.bts.gov/OSEA/TSI (accessed on 14 July 2020). 4. The World Bank/LPI Home. Available online: https://lpi.worldbank.org (accessed on 14 July 2020). 5. Agility Emerging Markets Logistics Index 2019. Available online: https://www.agility.com/insights/emerging- markets-logistics-index/overview/#:~{}:text=The%20survey%20of%20more%20than,conditions%2C% 20infrastructure%20and%20transport%20connections./ (accessed on 14 July 2020). 6. Urban Mobility Index. Available online: https://urbanmobilityindex.here.com (accessed on 14 July 2020). 7. The Siemens’ Green Cities Index. Available online: https://apps.espon.eu/etms/index.php/this-big-city/qr/ 534-siemens-green-cities-index (accessed on 14 July 2020). 8. Overall Global Mobility Ranking. Available online: https://www.arcadis.com/en/united-states/our- perspectives/sustainable-cities-mobility-index-2017/cities/?tf=tab-overall&sf=all&r=all&c=all] (accessed on 14 July 2020). 9. The Deloitte City Mobility Index. Available online: https://www2.deloitte.com/us/en/insights/focus/future- of-mobility/deloitte-urban-mobility-index-for-cities.html (accessed on 14 July 2020). 10. TomTom Traffic Index. Available online: https://www.tomtom.com/en_gb/traffic-index/ (accessed on 14 July 2020). 11. INRIX. Available online: http://inrix.com (accessed on 14 July 2020). 12. Travel Time Index. Available online: https://www.bts.gov/content/travel-time-index (accessed on 14 July 2020). 13. Casey, J.; Norwood, J.L. (Eds.) Key Transportation Indicators: Summary of a Workshop; National Academy Press: Washington, DC, USA, 2002; p. 41. Available online: http://www.nap.edu/catalog/10404 (accessed on 14 July 2020). 14. Imran, M.; Low, N. Time to Change the Old Paradigm: Promoting Sustainable Urban Transport. World Transp. Policy Pract. 2003, 9, 32–39. 15. Barker, W. Can a Sustainable Transportation System Be Developed for San Antonio, Texas. Transp. Res. Rec. J. Transp. Res. Board. 2005, 1924, 120–128. [CrossRef] 16. Nicolas, J.P.; Pochet, P.; Poimboeuf, H. Towards sustainable mobility indicators: Application to the Lyons conurbation. Transp. Policy 2003, 10, 197–208. [CrossRef] Sustainability 2020, 12, 7395 23 of 23

17. Kaparias, I.; Eden, N.; Tsakarestos, A.; Gal-Tzur, A.; Gerstenberger, M.; Hoadley, S.; Lefebvre, P.; LeDoux, J.; Bell, M. Development and Application of an Evaluation Framework for Urban Traffic Management and Intelligent Transport Systems. Procedia Soc. Behav. Sci. 2012, 48, 3102–3112. [CrossRef] 18. Kaparias, I.; Bell, M.G.; Gal-Tzur, A.; Komar, O.; Prato, C.; Tartakovsky, L.; Aronov, B.; Zvirin, Y.; Gerstenberger, M.; Tsakarestos, A.; et al. Key Performance Indicators for Traffic Management and Intelligent Transport Systems Deliverable. 2011. Available online: https://pdfs.semanticscholar.org/0fe7/3b4a8b1939fc90c88c693b6a2905b0e939b1.pdf?_ga=2.220451547. 293995317.1594762412-7263041.1592134505 (accessed on 14 July 2020). 19. Hoogendoorn, S.; Knoop, V. Traffic flow theory and modelling. In Transport System and Transport Policy, 1st ed.; van Wee, B., Annema, J.A., Banister, D., Eds.; Edward Elgar Publishing: Cheltenham, UK, 2013; pp. 125–159. 20. Aarts, L.; Schagen, I.V. Driving Speed and the Risk of Road Crashes a Review. Accid. Anal. Prev. 2006, 38, 215–224. [CrossRef][PubMed] 21. Rogdriguez, R.J. Speed. Speed Dispersion, and the Highway Fatality Rate. South. Econ. J. 1990, 57, 349–357. [CrossRef] 22. Yandex. Probki. Available online: https://yandex.ru/maps/213/moscow/?l=trf%2Ctrfe&ll=37.622504%2C55. 753215&z=10 (accessed on 14 July 2020). 23. He, F.F.; Yan, X.D.; Liu, Y.; Ma, L. A Traffic Congestion Assessment Method for Urban Road Networks Based on Speed Performance Index. Procedia Eng. 2016, 137, 425–433. [CrossRef] 24. Su, F.; Dong, H.; Jia, L.; Sun, X. On urban road traffic state evaluation index system and method. Mod. Phys. Lett. B 2017, 31, 1650428. [CrossRef] 25. Hansen, W.G. How Accessibility Shapes Land Use. J. Am. Inst. Plan. 1959, 25, 73–76. [CrossRef] 26. Taylor, M.A. Critical transport infrastructure in Urban areas: Impacts of traffic incidents assessed using accessibility-based network vulnerability analysis. Growth Chang. 2008, 39, 593–616. [CrossRef] 27. Taylor, M.A. Dense network traffic models, travel time reliability and traffic management II: Application to reliability. J. Adv. Transp. 1999, 33, 235–251. [CrossRef] 28. Schmidt, A.J. The Architecture and Planning of Classical Moscow: A Cultural History, 1st ed.; American Philosophical Society: Philadelphia, PA, USA, 1989; p. 218. 29. Dimitrijević,B.; Simic, V.; Radonjic, V.; Kostic-Ljubisavljevic, A. The Delphi Method as a Research Tool: An Application in Transportation and Logistics Systems Evaluations. In Proceedings of the 6th International Quality Conference, Center for Quality, Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia, 8 June 2012. [CrossRef] 30. Official Site of the Mayor of Moscow/My Street. (In Russian). Available online: https://www.mos.ru/city/ projects/mystreet (accessed on 14 July 2020).

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).