Generating Route Choice Sets with Operation Information on Metro Networks

journal of trafﬁc and transportation engineering (english edition) 2016; x (x): 1e10

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Original Research Paper Generating route choice sets with operation information on metro networks

* Wei Zhu a,b, , Ruihua Xu a,b a College of Transportation Engineering, Tongji University, Shanghai 201804, China b Key Laboratory of Road and Trafﬁc Engineering of Ministry of Education, Tongji University, Shanghai 201804, China article info abstract

Article history: In recent years, the metro system has advanced into an efficient transport system and Available online xxx become the mainstay of urban passenger transport in many mega-cities. Passenger flow is the foundation of making and coordinating operation plans for the metro system, and therefore, a variety of studies were conducted on transit assignment models. Nevertheless Keywords: route choice sets of passengers also play a paramount role in flow estimation and demand Metro prediction. This paper first discusses the main route constraints of which the train Route choice set schedule is the most important, that distinguish rail networks from road networks. Then, a Train operation information two-step approach to generate route choice set in a metro network is proposed. Particu- Generation approach larly, the improved approach introduces a route filtering with train operational information Two-step approach based on the conventional method. An initial numerical test shows that the proposed approach gives more reasonable route choice sets for scheduled metro networks, and, consequently, obtains more accurate results from passenger flow assignment. Recom- mendations for possible opportunities to apply this approach to metro operations are also provided, including its integration into a metro passenger flow assignment and simulation system in practice to help metro authorities provide more precise guidance information for passengers to travel. © 2016 Periodical Offices of Chang'an University. Production and hosting by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

present, people pay close attention to the operation and 1. Introduction management of the metro system because its availability and service state directly influence the city's normal activity and As an efficient transport system, the metro system has population. As known, passenger flow is the foundation of advanced in recent years and become the mainstay of urban making and coordinating operation plans for the metro sys- passenger transport in many mega-cities, especially in those tem. Several research studies have been carried out on the highly populated areas (Liu et al., 2010; Zhu et al., 2013). At

* Corresponding author. Tel.: þ86 13621854009. E-mail addresses: [email protected] (W. Zhu), [email protected] (R. Xu). Peer review under responsibility of Periodical Offices of Chang'an University. http://dx.doi.org/10.1016/j.jtte.2016.05.001 2095-7564/© 2016 Periodical Offices of Chang'an University. Production and hosting by Elsevier B.V. on behalf of Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Zhu, W., Xu, R., Generating route choice sets with operation information on metro networks, Journal of Traffic and Transportation Engineering (English Edition) (2016), http://dx.doi.org/10.1016/j.jtte.2016.05.001 2 journal of traffic and transportation engineering (english edition) 2016; x (x): 1e10

development of passenger flow assignment models, which describe this study's general scope. In Section 3, the main have become more and more complex in obtaining precise constraints, including the network topology, travel cost, and, results. However, the route choice set of passengers also plays especially, the train operational plan, which influence the a crucial role in analyzing passenger flows. A variety of studies size and, composition of route choice sets are analyzed. have found that the size and composition of a route choice set Then, a feasible approach to generate the route choice sets does matter in the choice model estimation and passenger in metro networks is proposed in detail in Section 4.A flow calculation (Bovy, 2009; Ren et al., 2012; Swait and Ben- numerical test is given to demonstrate the application and Akiva, 1986, 1987). Incorrect route choice sets can lead to efficiency of the proposed approach. Moreover, misspecification of choice models and calculation biases recommendations for application of the proposed approach passenger flow levels (Ortuzar and Willumsen, 2001; Williams to metro operation are provided. Finally, Section 6 and Ortuzar, 1982). summarizes the paper's finding. This paper focuses on the modeling and generation of route choice sets for metro networks. There are a number of completed research topics related to it, and the network type 2. Passenger flow assignments and route for which this subject is relevant is nearly limitless. Inter- choice sets esting studies and applications exist for pedestrian networks (Van Der Waerden et al., 2004), public transport (Friedrich Thus far, models used to solve passenger flow assignment et al., 2001), roads (Prato and Bekhor, 2006), multimodal problems are classified according to whether Wardrop's networks (Bovy and Hoogendoorn-Lanser, 2005), inland principle is followed. One model is the non-equilibrium navigation (Van Der Zijpp and Fiorenzo-Catalano, 2005), assignment, and another is the equilibrium assignment airlines (Coldren and Koppelman, 2005), and buildings (Lo model. Both of them can be solved either in the space of link and Fang, 2000). The Literature shows many different flows or in the space of route flows (Bekhor and Toledo, 2005). techniques for generating routes, such as recent study The important advantage of the link-based solution published by Liu et al. (2010). Typical approaches include the algorithms is that they can avoid explicit enumeration of the K-shortest path algorithm (Van Der Zijpp and Fiorenzo route choice sets. However, the link-based algorithms Catalano, 2002) based on link elimination or link penalties assume an implicit choice set, such as the use of all efficient (De La Barra et al., 1993), simulation methods (Nielsen, 1996, paths (Dial, 2001, 2006; Maher, 1998) or all cyclic and acyclic 2002; Sheffi and Powell, 1982), and a labeling approach (Ben- paths (Akamatsu, 1996; Bell, 1995), which may be unrealistic Akiva et al., 1984). However, few research has been devoted from a behavioral standpoint. On the contrary, the route- to rail networks, particularly metro networks. To the best of based solution algorithms allow a more flexible definition of the authors' knowledge, most methods currently used in the choice set (Bekhor and Toledo, 2005; Ben-Akiva et al., practical demand analyses to generate route choice sets in 1984; Cascetta et al., 1997), but the choice set generation rail networks are based on network attributes and passenger methods increase computation time. In any case, the route preferences (Ben-Elia et al., 2013; Kato et al., 2010; Liao et al., choice set does play an important role in solving passenger 2013; Nielsen, 1996, 2002; Ramming, 2002). Compared to road flow assignment problems. and other network modes, the generation of route choice To clarify the article's scope, Manski's (1977) paradigm on sets in rail networks is based not only on the physical predicting choice, although debated in the literature, is network topology, and passenger preferences (e.g., time, helpful. This paradigm states that the probability of distance, etc.) but also on the train operational plans, passenger i to choose alternative r from the choice set CSi, making the problem different and more complex. For which is also called his/her consideration set, is given by the example, due to the train diagram, the accessibility of a following expression. given OeD pair in the rail network changes dynamically X ð j Þ¼ ð j Þ ð j Þ throughout a day. Pi r USi pi r CSi p CSi USi (1) CS 2US The objective of this study is to provide an approach that i i can generate accurate route choice sets in metro networks where PiðrjUSiÞ is the probability that passenger i will choose considering the influences from the network topology, travel alternative r from the universal set USi of all alternatives cost, and, especially, the train operational information. This available to i, piðrjCSiÞ is the conditional probability that pas- paper proposes the advances as follow. senger i will choose alternative r given that CSi is his/her

consideration set where CSi is a subset of USi, pðCSijUSiÞ is the

(1) Provide a route ﬁltering method based on the travel cost probability that CSi is the consideration set of passenger i

difference between an alternative route and the short- given his/her universal set USi. est route. An essential element of this paradigm is that the choice set (2) Propose a route ﬁltering method based on train opera- of an individual cannot, for the most part, be known with tional plans. certainty nor be observed, so it is a latent concept that might

(3) Put forward a two-step framework to generate route be modeled as a random set as expressed by p(CSi). Although choice sets on metro networks, based on both of the the random choice set concept seems very natural, nearly all mentioned above. choice modeling applications in research and practice adopt a ð j Þ¼ ð j 4 Þ deterministic choice set approach, pi r USi pi r CSi USi .In To this end, the paper ﬁrst explains how route choice sets most cases, as certainly in route choice, the choice set is a play an important role in passenger ﬂow assignments to subset of a universal set of available alternatives. This subset's

speciﬁcation and generation from the universal set is the are possible routes and train runs for him/her in terms of subject of this paper. network topology, travel cost, and train operational plan. The train operational data, combined with those passenger behavior parameters, can provide additional useful informa- 3. Route constraints for metro networks tion to generate passenger route choice set more precisely.

3.1. Network topology 4. Two-step approach for route choice set A metro network's physical topology consists of rail links generation and rail stations, which is the basic constraint for passenger route choices in the network. With the developments of 4.1. Overall framework metro systems, the network's expansion has enabled more flexible and complex travels within the metro system Taking the constraint of train operational information into because more transfer stations are providing better con- account, an improved approach is introduced to generate necting services. In other words, in a metro network, there reasonable route choice sets (Fig. 1). According to the route are different connecting routes for a passenger to choose for constraints of metro networks described in Section 3, the e e agivenorigin destination (O D) pair. The physical topology authors conceive a model-based choice set generation to based search, consequently, results in the generation of a consist of two steps as follows. universal set of routes, which serves as the initial route set and is filtered by other constraints such as travel cost, train Step 1. Universal route choice set generation. This is a basic operational plan, etc. step. In this step, a universal route choice set is generated based on the physical topology of the metro network. It is 3.2. Travel cost noticed that the size and composition of the universal route choice set, in the case of a large metro network, may Travel cost, which can be travel distance, travel time, or so be unknown to the analyst. called generalized cost, is another constraint in passenger Step 2. Constraint based filtering. This is a special step for route choices in the metro network. As the mentioned above, an improved approach. In this step, the so called initial ' due to the metro network s physical topology, there may be filtering is performed and the universal route choice set is e several alternative routes for an O D pair, and passengers, in narrowed to a consideration route choice set by applying a practice, will choose not only the shortest route but also the set of constraints, including travel cost constraint and, … second, third, , kth shortest path. However, there is no particularly, train operational plan constraint, to the uni- benefit to enumerating routes (e.g., universal route sets) that versal route choice sets. no passenger would consider. Moreover, as shown in travel surveys (Zhu, 2011) of URT passengers, if the difference in travel cost between an alternative route and the shortest 4.2. Universal route choice set generation route exceeds a specific threshold, there is little possibility passengers will choose alternative route. Due to the metro system's network operation, there may be several alternative rail routes for an OeD pair, and passengers, 3.3. Train operational plan in practice, will choose not only the shortest route but also the second, third, …, kth shortest route due to imperfect knowl- Different from road networks, trains in the metro network run edge of the network, individual differences, factor of conges- strictly according to their operational plans. In practice, a train tion, etc. Firstly, an improved Deletion Algorithm (DA) based diagram is a graphic illustration of the train's operational on Depth-first Traversal (DFT) is introduced to find the kth plans, providing train running times, routes, and schedule of shortest route, and the initial choice set (e.g., universal route the first and last runs, all of which will influence the accessi- choice set) of a given OeD pair is obtained. Detailed infor- bility of any given OeD pair in the URT network during mation on the improved DA can be found in authors' previous different periods of a day. Thus, the factor of train's opera- reference (Xu et al., 2009). tional state, which is an important constraint for route choice set generation, cannot be neglected in influencing passenger 4.3. Constraint based route filtering route choices.

4.3.1. Filtering based on travel cost 3.4. Passenger behavior parameters There is no beneﬁt to enumerating routes that no passenger would consider. And there is little possibility that the alter- Certain parameters of passenger travel behavior, such as native route will be chosen by passengers if the travel cost transfer walking and waiting for trains also serve as the con- difference between an alternative route and the shortest route straints in generating his/her feasible route choice sets. exceeds a certain threshold. The threshold can be calculated Especially, the parameter of the passenger's transfer walking as follow speed is an important factor among the various behavior parameters. A passenger cannot board his/her expected train if D rs ¼ d rs ; D GCmax min GCmin GC (2) he/she walks too slowly to miss the train, even though there

Fig. 1 e Framework of two-step approach for route choice set generation.

D rs where GCmax is the travel cost threshold difference between transfer between different rail lines occurs. During the trip, an alternative route and the shortest route for the OeD pair the automated fare collection (AFC) system registers both rs e (rs), GCmin is the travel cost of the shortest route for the O D transactions, recorded and times-tamped at the entry and exit pair (rs), d and DGC are a proportion coefficient and a constant points. with the same unit to travel cost, respectively, both of which Based on the analysis, a typical passenger's travel chain can be decided by the result of travel survey for metro can be divided into five parts: walking to the original station's passengers. platform, waiting for the train, traveling on the train, waiting Based on the mentioned above, the universal set obtained at the transfer station(s), and walking out of the destination in Step 1 can be filtered after judging the rationality of alter- station from the platform. This passenger's travel chain is native routes based on the threshold of travel cost difference illustrated in Fig. 2. between the alternative route and the shortest route. (2) Route's last boarding plan 4.3.2. Filtering based on train operational plan A passenger completes his/her trip from the original sta- (1) Passenger travel chain tion to the destination station by taking certain trains, which can be called boarding plans. A boarding plan is the order of One of this study's basic philosophies is that the passen- trains that the passenger can take to complete his/her travel. ger's travel time depends on the interaction between travel Particularly, among those boarding plans, the last boarding route and train operation. If the interaction mechanism is plan is paid special attention to filter routes. As seen from the understood, then the passenger route choice set can be passenger's travel chain, for a given trip data obtained from determined more precisely. the metro system, a route is considered unreasonable and When the authors investigated the travel process of metro should be removed from the universal route choice set if its passengers, a general travel can be described. Firstly, a pas- last boarding plan is impossible for the passenger to complete senger uses his/her smart card to enter the gate; then, he/she given the constraints of his/her entry and exit times. gets through the access passenger way to arrive at the plat- The last boarding plan of a route can be deduced from the form to wait for the train that he/she wants to board. When a passenger's trip chain, combined with the train diagram and train with enough capacity to accommodate passengers ar- passenger's actual entry and exit times. With no loss of gen- rives at the platform, that passenger will ride this train to his/ erality, the following relevant notations of algorithm her expected station platform. Afterwards, he/she walks to description are introduced in advance. the exit gates and again swipes his/her card to finish the travel tO, tD are the entry time at the original station and the exit experience. An additional transfer stage must be included if a time at the destination station, respectively, tO,f is the

Fig. 2 e Illustration of the passenger's travel chain.

passenger's walking time with the fastest speed from the card possible train Ti,ki which the passenger could take to gate to the platform at the original station, tD,f is the passen- transfer. ' ¼ d d … d ger s walking time with the fastest speed from the platform to Step 4. If i 1, the boarding plan [Ti,ki Tiþ1,kiþ1 Tn,k]is the card gate at the destination station, li is the ith rail line in the last possible boarding plan and algorithm ends. the route, i ¼ 1, …, n, where n is the number of rail lines in the Otherwise, proceed to Step 3. route, Ti,j indicates that the jth train can be taken of the ith rail line in the route, then a possible boarding plan can be This algorithm is illustrated in Fig. 3, which represents ¼ d d … d expressed as B [T1,j1 T2,j2 Tn,jn]. train diagrams. The last boarding plan can be expressed as The algorithm (e.g., Algorithm A) for deducing the route p's the set of thick lines. last boarding plan can be developed as follow. (3) Last boarding plan based ﬁltering algorithm Step 1. According to the passenger's exit time and the walking time with fastest speed at the destination station, Based on the mentioned above, the route choice set for a determine the last possible train which the passenger can given OeD pair can be further narrowed, and the ﬁltering al- get off. As shown in Fig. 3, the last possible train is the train gorithm (e.g., Algorithm B) is described as follow. Tn,k which is the closest before the time (tD tD,f). Suppose the calculation depth is i, i ¼ n. Step 1. Obtain the last possible boarding plan. For an actual Step 2. If i ¼ 1, there is no transfer in the route, the boarding passenger trip using the corresponding train diagram, the ' plan [Tn,k] is the last possible boarding plan and the algo- passenger s exit time and walking time at the destination rithm ends. Otherwise, i ¼ i1. station, as well as the last possible boarding plan for each Step 3. According to the passenger's walking time with route, can be deduced by Algorithm A. fastest speed at the transfer station, determine the last Step 2. Calculate the departure time of the last possible boarding plan. Based on the passenger's travel chain and

train diagrams, the departure time tdeparture of the last possible boarding plan of each route can be calculated working backwards from the destination station to the original station (Fig. 3). Step 3. Compare and remove. As shown in Fig. 4, the

calculated departure time (tdeparture) of the last possible boarding plan at the original station is compared with the ' þ passenger s arrival time (tO tO,f) on the platform. If þ < (tO tO,f) tdeparture, there is one or more possible boarding plan for the passenger to choose, and consequently, the corresponding route is reasonable. Otherwise, there is no possible boarding plan, and the corresponding route is removed from the route choice set.

5. Numerical experiment

5.1. Test OeD pair and route choice model

Fig. 3 e Illustration of how to deduce the route's last In order to test the choice set generation approach proposed in boarding plan. this paper, a numerical experiment was conducted on a speciﬁc

Fig. 4 e Comparisons of the calculated departure time of the last possible boarding plan versus the passenger's arrival time t þ t < t t þ t > t on the platform. (a) The route is reasonable (( O O,f) departure). (b) The route is unreasonable (( O O,f) departure).

OeD pair (from Xingzhi Rd. to Xinzhuang) in the Shanghai In this section, a route choice model derived from travel metro network. As shown in Fig. 5, there are three routes surveys and currently used in Chinese cities is introduced to connecting the original station (Xingzhi Rd.) and the estimate the passenger route choices. The model's main part destination station (Xinzhuang). The ﬁrst path moves through can be expressed as follow stations Dahuasan Rd., Xincun Rd., Langao Rd., Zhenping Rd., Urs Changshou Rd., Changping Rd., Jing An Temple, Changshu rs ¼ P k pk rs (3) Uk Rd., Hengshan Rd., Xujiahui, Shanghai Indoor Stadium, k Caobao Rd., Shanghai South Railway Station, Jinjiang Action

2 Park, Lianhua Rd., and Waihuan Rd., with the transfer ðDCrsÞ rs k ¼ 2s2 stationdChangshu Rd. The second path travels through Uk e (4) stations Dahuasan Rd., Xincun Rd., Langao Rd., Zhenping Rd., Changshou Rd., Changping Rd., Jing An Temple, Changshu GCrs GCrs DCrs ¼ k min k d rs ; D (5) Rd., Zhaojiabang Rd., Dongan Rd., Shanghai Stadium, min GCmin GC Shanghai Indoor Stadium, Caobao Rd., Shanghai South where prs is the choice probability of the route k for a given Railway Station, Jinjiang Action Park, Lianhua Rd., and k OeD pair (rs), Urs is the utility of the route k for the OeD pair Waihuan Rd., with the transfer stationsdDongan Rd. and k (rs), assuming the utility of the shortest route is the maximum Shanghai Indoor Stadium. The third path travels through and equals to 1, DCrs is the generalized travel cost difference stations Dahuasan Rd., Xincun Rd., Langao Rd., Zhenping Rd., k between the shortest route and route k, e approximately Changshou Rd., Changping Rd., Jing An Temple, Changshu equals to 2.718, s is the standard deviation of the normal Rd., Zhaojiabang Rd., Xujiahui, Shanghai Indoor Stadium, distribution, which is a constant to all the OeD pairs and can Caobao Rd., Shanghai South Railway Station, Jinjiang Action be analyzed and drafted through the results of travel surveys, Park, Lianhua Rd., and Waihuan Rd., with the transfer GCrs is the travel cost of the shortest route of the OeD pair stationsdZhaojiabang Rd. and Xujiahui. The theoretic travel min (rs), d and DGC are a proportion coefﬁcient and a constant with times of the three routes are 2970, 3485, and 3488 s, respectively.

Fig. 5 e Rail network connecting the test OeD pair (Xingzhi Rd. / Xinzhuang).

the same unit to travel cost, respectively, both of which can be the test OeD pair is reviewed in the whole Shanghai Metro decided by the result of travel survey for metro passengers, network, then there are more than ten routes in the rs e GCk is the generalized cost for the O D pair (rs). universal route choice set. However, in this numerical experiment, the authors focused on analyzing the influence 5.2. Data used in the test of train operational information on the route choice model. Using the input data mentioned above, the route choice Firstly, since the number of daily passengers using the model was performed with the route choice sets generated by Shanghai metro network is very large (more than 7,000,000) the conventional and the proposed approach, respectively. and the lab computers cannot process such a large sample The conventional approach generates the route choice set size well, 150 passenger trips records between 07:00 a.m. and without train operational information, and its detailed 08:00 a.m. and obtained from the AFC system are used to description can be referenced in Xu et al. (2009). The results verify the proposed approach. Table 1 gives a sample record were used to analyze the efficiencies of both choice set from these 150 passenger trips. generation approaches. Secondly, certain parameters of the route choice model Table 5 shows a comparison of the route choice results should also be estimated in advance. The rail-ride travel times calculated by the route choice model with the different route and train dwelling times at rail stations were induced from choice set generation approaches. As can be seen, the choice train schedules. The walking times for transferring were probability of Route 1 calculated by the proposed approach determined by the layout of the rail station. Waiting times in this paper is stronger than the choice probability of Route were calculated according to the train's headway, and the 1 calculated by conventional approach. It is because that, for hourly flow capacity, which can be used to calculate the in- some passenger trip records, both Routes 2 and 3 are not vehicle congestion rate of rail link, was decided based on the reasonable routes any more if the relevant train operational rail line's frequency and loading capacity. Information from information is considered. These results are also verified by train schedules, station layouts, train loading capacities, etc., the survey. Moreover, according to statistics from the test were provided by Operation Center of Shanghai Metro. In network, the proportion of OeD pairs for which there is addition, based on the travel survey results of Xu et al. (2007), unique route can be increased by more than 60% with d, DGC and a were set to 60%, 10 min and 1.5, respectively. a is additional filtering based on train operational information. a transfer magnification parameter which turns the transfer This finding is now partly used to test the currently-used time equally into the time on trains. passenger flow analysis and fare clearing models of metro Particularly, as the most important inputs of the approach systems in Beijing and Shanghai. to the route choice set generation in this paper, the train In summary, according to the Table 5, the proposed operational information of the relevant rail lines (e.g., Lines 1, approach for generating route choice sets in metro networks 7 and 9) were obtained. Tables 2e4 show the samples of this delivered the more accurate solutions. Although compared information. with the conventional methods, its additional filtering based on train operational information results in higher 5.3. Results and analysis computation time, the proposed approach is still acceptable, as the hardware and software improve. In particular, the It should be noted that the three routes connecting the test computational efficiency of this study's approach can be OeD pair shown in Fig. 5 are considered reasonable routes. If greatly improved by the application of parallel and computing techniques.

Table 1 e Samples of passenger trip records. No. Original Destination Passenger Passenger station station entry time exit time 6. Opportunities for applying the proposed 1 Xingzhi Rd. Xinzhuang 07:00:06 07:50:47 approach to metro operation 2 Xingzhi Rd. Xinzhuang 07:00:05 07:51:00 3 Xingzhi Rd. Xinzhuang 07:21:07 08:12:45 Facing to the stage of operation for metro systems, this paper 4 Xingzhi Rd. Xinzhuang 07:25:51 08:19:05 proposes an approach for generating route choice sets that 5 Xingzhi Rd. Xinzhuang 07:26:05 08:18:45 consider the inﬂuences from the network topology, travel «« « « « cost, and, especially, the train operational information, which

Table 2 e Train schedule of Route 1. Xingzhi Rd. of Line 7 Changshu Rd. of Line 7 Changshu Rd. of Line 1 Xinzhuang of Line 1 Arrival Departure Arrival Departure Arrival Departure Arrival Departure 07:01:30 07:01:45 07:19:42 07:20:06 07:24:43 07:25:01 07:46:47 e 07:07:30 07:07:45 07:25:42 07:26:06 07:29:00 07:29:18 07:51:04 e 07:13:30 07:13:45 07:31:42 07:32:06 07:31:26 07:31:44 07:53:30 e 07:19:30 07:19:45 07:37:42 07:38:06 07:33:52 07:34:10 07:55:56 e 07:22:45 07:23:00 07:40:57 07:41:21 07:36:18 07:36:36 07:58:22 e ««««««« «

Please cite this article in press as: Zhu, W., Xu, R., Generating route choice sets with operation information on metro networks, Journal of Traffic and Transportation Engineering (English Edition) (2016), http://dx.doi.org/10.1016/j.jtte.2016.05.001 8 laect hsatcei rs s h,W,X,R,Gnrtn ot hiest ihoeainifraino er networks, metro on information http://dx.doi.org/10.1016/j.jtte.2016.05.001 operation with (2016), sets Edition) choice (English route Engineering Generating Transportation R., Xu, and W., Traffic Zhu, of as: Journal press in article this cite Please ora ftafi n rnprainen transportation and traffic of journal

Table 3 e Train schedule of Route 2. Xingzhi Rd. of Line 7 Dongan Rd. of Line 7 Dongan Rd. of Line 4 Shanghai Indoor Shanghai Indoor Xinzhuang of Line 1 Stadium of Line 4 Stadium of Line 1 Arrival Departure Arrival Departure Arrival Departure Arrival Departure Arrival Departure Arrival Departure 07:01:30 07:01:45 07:24:27 07:24:42 07:28:41 07:28:56 07:32:32 07:33:00 07:36:00 07:36:27 07:51:04 e 07:07:30 07:07:45 07:30:27 07:30:42 07:33:41 07:33:56 07:37:32 07:38:00 07:38:26 07:38:53 07:53:30 e 07:13:30 07:13:45 07:36:27 07:36:42 07:38:41 07:38:56 07:42:32 07:43:00 07:40:52 07:41:19 07:55:56 e 07:19:30 07:19:45 07:42:27 07:42:42 07:43:41 07:43:56 07:47:32 07:48:00 07:43:18 07:43:45 07:58:22 e 07:22:45 07:23:00 07:45:42 07:45:57 07:48:41 07:48:56 07:52:32 07:53:00 07:45:44 07:46:11 08:00:48 e ««««««««««« « iern egihe (english gineering

Table 4 e Train schedule of Route 3.

Xingzhi Rd. of Line 7 Zhaojiabang Rd. of Line Zhaojiabang Rd. of Line Xujiahui of Line 9 Xujiahui of Line 1 Xinzhuang of Line 1 1 (x): x 2016; dition) 7 9 Arrival Departure Arrival Departure Arrival Departure Arrival Departure Arrival Departure Arrival Departure 07:01:30 07:01:45 07:22:25 07:22:43 07:27:43 07:28:07 07:30:48 07:31:06 07:33:03 07:33:30 07:51:04 e 07:07:30 07:07:45 07:28:25 07:28:43 07:31:43 07:32:07 07:34:48 07:35:06 07:35:29 07:35:56 07:53:30 e 07:13:30 07:13:45 07:34:25 07:34:43 07:35:43 07:36:07 07:38:48 07:39:06 07:37:55 07:38:22 07:55:56 e 07:19:30 07:19:45 07:40:25 07:40:43 07:39:43 07:40:07 07:42:48 07:43:06 07:40:21 07:40:48 07:58:22 e 07:22:45 07:23:00 07:43:40 07:43:58 07:43:43 07:44:07 07:46:48 07:47:06 07:42:47 07:43:14 08:00:48 e ««««««««««« « e 10 journal of trafﬁc and transportation engineering (english edition) 2016; x (x): 1e10 9

Table 5 e Comparison of the route choice results calculated by conventional and proposed modeling approach to route choice set generation. Route no. Route description Conventional approach Proposed approach Passengers Proportion (%) Passengers Proportion (%) 1 Line 7 / Line 1 78 52 91 61 2 Line 7 / Line 4 / Line 1 36 24 30 20 3 Line 7 / Line 9 / Line 1 36 24 29 19

is different from the planning methods of other transport more than 60% with additional filtering based on train oper- modes. Meanwhile, the proposed approach in this paper has ational information. This finding is now partly used in current important implications for metro operation. passenger flow analyses and fare clearing models of metro As noted in the Section 5, combining with the train systems in Beijing and Shanghai. operational information, the proposed approach can The proposed approach is beneficial to improve the current generate more reasonable route choice sets and operation and management of Chinese metro networks. It can consequently, can be used to provide more accurate route be integrated into a metro passenger flow assignment and choice results. Instead of current methods to generate route simulation system, and help metro authorities provide more choice sets in URT networks, the proposed approach has the precise guidance information for passengers to travel. potential to improve the accuracy of passenger flow analysis and fare clearing, which are the foundations of the metro operation. In fact, the authors are making a tentative attempt to update the metro passenger flow assignment and Acknowledgments simulation system with the proposed approach in Beijing, China. This study is financially supported by the National Science On the other hand, the dynamic accessibility between a and Technology Support Program of China (2011BAG01B01), given OeD pair mainly depends on the physical network, as Program for Young Excellent Talents at Tongji University well as on the train operational network. The proposed (2014KJ015), Shanghai Philosophy and Social Science Funds approach, therefore, can help to analyze the accessibility co- (2015EGL006), and Fundamental Research Funds for the Cen- ordination scheme of the metro system especially for the first tral Universities of China (1600219249). The authors wish to and last trains, which plays a significant role in promoting the express additional appreciation to the Shanghai Shentong intelligent operation of the metro system. Furthermore, with Metro Co., Ltd., for providing useful information. the accessibility coordination scheme, metro authorities can provide more precise guidance information for passengers to references travel. This is the subject of further research in the future.

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