IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. X, NO. X, MONTH YEAR 1 Empirical analysis of the variability in the flow-density relationship for smart motorways Kieran Kalair and Colm Connaughton Abstract—The fundamental diagram is an assumed func- significant variability that is not captured by a single tional relationship between traffic flow and traffic density. functional form. As a result, it is natural to consider In practice, this relationship is noisy and exhibits significant a distributional interpretation of the density-flow rela- statistical variability. On smart motorways, this variability is increased by variable speed limits that are not captured tionship, considering it as a 2-dimensional probability by the fundamental diagram. To study this variability, it is distribution and analyzing features of this to provide a appropriate to consider the joint probability distribution different perspective on the relationship between density function (pdf) of density and flow. We perform an empirical and flow. In this work, we take such an approach to study of the variability in the relationship between flow and analyse the empirical variation in fundamental diagrams density using 74 days of data from 64 sections of London?s M25. The objectives are to determine how much of the obtained from London’s M25, one of the busiest Smart variability in the flow-density relationship results from Motorways in the UK. In addition to studying the variable speed limits and to assess whether particular func- variability of the density-flow relationship at a single tional forms of the fundamental diagram are systematically location, we also consider variation between locations. preferred. Empirically, the joint pdf of flow and density is We approach this by first determining the optimal func- strongly bimodal, illustrating that traffic flows are often found in high-density or low-density regimes but rarely in tional form for a diagram across 64 sections of road between. We find that the high-density regime is strongly on the M25, then use clustering analysis to assess what affected by variable speed limits whereas the low-density qualitatively different types of density-flow relationships regime is not. The Daganzo-Newell (triangular) model of emerge in the network. This provides insights into the the fundamental diagram systematically fits best to the variability in the density-flow relationship of smart- data. However, the optimal parameters vary with location. Clustering analysis of these parameters suggests three motorways on both a local and global scale. qualitatively different types of flow-density relationships applying to different sections of the M25. These clusters II. LITERATURE REVIEW have natural interpretations in terms of the frequency and Fundamental diagrams have been widely studied, severity of flow breakdown. Accident rates also depend on cluster type suggesting possible links to other properties of starting with the first model of the density-speed re- traffic flows beyond the flow-density relationship. lationship formulated by Greenshields in 1933 [11]. As an observation of a complex system, the diagrams Index Terms—Big Data, Fundamental Diagrams, Varia- tion, Spatial Analysis, Variable Speed Limits contain much complexity, commonly exhibiting different levels of variation and spread of data as density varies. Many attempts to study fundamental diagrams have been I. INTRODUCTION conducted since their inception. One such work is [18], He fundamental diagram of traffic flow is an im- where the authors took a year of data and proposed arXiv:1903.05112v1 [cs.CY] 12 Mar 2019 T portant tool in understanding, categorizing and ana- the optimal functional form of the fundamental diagram lyzing traffic flow. Despite several models and functional from this data was the logistic diagram. Whilst most forms of the density-flow and density-speed relation- works take a piecewise triangular functional representa- ships being suggested over the years, understanding the tion of a fundamental diagram, their search for, in their complex structure of fundamental diagrams is a difficult words, a functional form with ‘mathematical elegance task. Furthermore, analysis of real data suggests that the and empirical accuracy’ demonstrates that even a simple density-flow relationship in real traffic systems exhibits question of what curve fits a set of data points best is open to discussion. Manuscript received XX-XX-XX; revised XX-XX-XX; accepted Many sources of variation observed in the fundamental XX-XX-XX. Date of publication XX-XX-XX; date of current version XX-XX-XX. This work was supported in part by the EPSRC and MRC diagram are incorporated when combining the diagrams Centre for Doctoral Training in Mathematics for Real World Systems with models, either macroscopic or microscopic. One under grant number EP/L015374/1. example of this is defining the flux function of a K. Kalair and C. Connaughton are based at the Centre for Com- plexity Science, University of Warwick, Coventry CV4 7AL, United macroscopic model (which is simply the fundamental Kingdom. diagram) to have a random free-flow speed [12]. Whilst IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. X, NO. X, MONTH YEAR 2 this captures some of the variation observed in real of lanes is constant and no slip roads join or leave the data and incorporates it in model results, it does not motorway. We first recreate this directed graph model address the initial question of quantifying or better un- for the M25, consisting of 77 links, varying in length derstanding the variation present in original data. Other between 200 and 10,000 meters. works have argued that simply including random noise Placed on each link are physical sensors called ‘loops’. or terms in initially deterministic models can lead to Loops detect passing vehicles at various distances along behaviour inconsistent with the original model, which links, and from this the following measurements of lead to [8] rephrasing this randomness in a Lagrangian interest are recorded: framework model, essentially allowing driver headway - Speed: The average speed of vehicles crossing the choice to be stochastic. Increasingly, instead of random loop in kilometers per hour flux functions, some modelling approaches use real-time - Flow: The number of vehicles crossing the loop in data to update models which attempts to account for a time-period in vehicles per hour the deviations from typical behaviour, for example in We compute a time-series of density by dividing flow by [5] and [20]. Whilst useful, especially as more data speed. NTIS aggregates this loop-level data to produce and processing power becomes readily available, this link-level recordings of speed and flow, among other again does not attempt to better understand the variation quantities not used in this study. The link-level data is present before any modelling of traffic is completed. the resolution we focus on here, meaning we extract one Whilst our work focuses on fundamental diagrams of data-point measuring speed and flow every minute for 74 smart motorways, there is much work focusing on deriv- days, repeated for 77 different links. ing them for an urban setting, for example [16] and [17]. Whilst the first paper here attempts to create an algorithm In addition, we extract event and speed limit informa- to fit functions to the sparse probe data, and the second tion. Events are any unusual activity on a link, separated attempts to use the probe data to build a model of the into the following categories: traffic state, neither address the problem of variation in - Accidents the urban sense. Whilst discovering appropriate ways to - Vehicle obstructions use new data sources is important in the context of traffic - General obstructions management in urban scenarios, it is also important to - Abnormal traffic better understand the situations where data collection - Maintenance, Road management & poor environ- is more routine and simple, for example on sensor mental conditions equipped motorways. It may be an interesting extension We focus on accidents, vehicle obstructions, general of our work to consider if there exist any significant obstructions and abnormal traffic events. NTIS defines an differences in the urban level-variation compared to the abnormal traffic event as a period when the true travel more controlled and dense motorway traffic variation. time on a link exceeds Highways England’s predicted Perhaps a work most similar to ours is [15], where the travel time by at least 120 seconds, for at least 5 authors investigate the impact of variable speed limits consecutive minutes. The algorithm used to compute on the slope of a fundamental diagram, as well as the travel times is not publicly known. critical occupancy and capacity of a road. Our work Speed limit information represents operator interven- differs substantially from this, as we take different in- tions on motorway traffic. On some links on the M25, terpretation of the fundamental diagram on a single link adjustable speed limit signs are displayed overhead to to analyse the impact of variable speed limits, through a drivers, which can be used to temporarily change the 2d probability distribution and clustering approach, not speed limit on a section of road to any of 40, 50, 60 or a functional form approach. In addition, we consider the 70 miles per hour. spatial consistency of different fundamental diagrams, After processing, we have a set of time-series, with something not covered in this paper flags at each time there was an event or speed limit intervention on a link. Of the 77 links, 64 have working III. DATA PRE-PROCESSING sensors and report data we can use for analysis. The data for this study is taken from Highways England’s National Traffic Information Service (NTIS). IV. EXPLORATORY ANALYSIS We choose to focus on the M25 motorway, one of the busiest in the country, taking 74 days of data starting Typically, one views a fundamental diagram as a from April 7th 2017.