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Recovery measure of disruption in train operation in Tokyo Metropolitan Area
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Transportation Transportation Research Research Procedia Procedia 00 00 (2017) (2017) 000–000 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 25C (2017) 4374–4384 www.elsevier.com/locate/procedia
World Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016
Recovery measure of disruption in train operation in Tokyo Metropolitan Area HIBINO Naohikoaa, NAGAOKA Osamuaa, MORICHI Shigeruaa, IEDA Hitoshiaa, Tomii Noriobb a a
National 7-22-1 Roppongi, National Graduate Graduate Institute Institute for for Policy Policy Studies, Studies, 7-22-1 Roppongi, Minato-ku, Minato-ku, Tokyo Tokyo 106-8677, 106-8677, Japan Japan b bChiba Intsitute of Techonology, 2-17-1, Tsudanuma, Narashino-shi Chiba 275-0016, Japan Chiba Intsitute of Techonology, 2-17-1, Tsudanuma, Narashino-shi Chiba 275-0016, Japan
Abstract Abstract
In Metropolitan Area, Area, there there is is one one of of the the densest densest railway railway networks networks in in the the world. world. Passengers Passengers are are provided provided with with In Tokyo Tokyo Metropolitan the high high level level services services such such as as mutual mutual direct direct and and high high frequent frequent train train operation. operation. The The passengers, passengers, however, however, suffer suffer from from the the negative negative impact impact such such as as inconvenience inconvenience when when train train traffic traffic is is disrupted disrupted by by an an accident. accident. In In particular, particular, during during peak peak the hours, they have to commute in a congested train for long time. This is a kind of paradox and a serious social hours, they have to commute in a congested train for long time. This is a kind of paradox and a serious social problem which which should should be be solved solved immediately. immediately. The The objective objective of of this this study study is is to to visualize visualize the the results results of of train train operation operation problem when when train train traffic traffic is is disrupted disrupted on on the the basis basis of of the the actual actual data. data. The The study study illustrates illustrates the the several several cases cases when when trains trains are are seriously seriously delayed delayed by by using using actual actual result result operation operation data data in in order order to to analyse analyse the the difference difference caused caused by by the the factors factors such such as as circumstances circumstances of of the the companies, companies, situations situations of of the the accident accident and and railway railway line, line, duration duration of of accident accident and and so so on. on.
© © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Peer-review under responsibility of WORLD CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY. Peer-review under responsibility of WORLD CONFERENCE Keywords: Keywords: train train delay, delay, interview interview survey, survey, train train operation operation record record data, data, delay delay certificates, certificates, visualization visualization
1. Introduction 1. Introduction In Tokyo Tokyo Metropolitan Metropolitan Area Area (hereinafter (hereinafter TMA), TMA), aa huge huge number number of of passengers passengers are are transferred transferred by by the the railway railway In network everyday network everyday over over aa wide wide area. area. Today, Today, TMA’s TMA’s railway railway network network is is known known as as one one of of the the world’s world’s leading leading transport transport systems with reliable, reliable, safe safe and and convenient convenient operation. operation. Service Service quality quality of of the the systems in in handling handling aa huge huge traffic traffic volume volume with network developing aa high high network has has been been improved improved constantly constantly through through the the following following measures: measures: using using long long train-sets, train-sets, developing density density railway railway network, network, operating operating trains trains with with aa high high frequency, frequency, sharing sharing tracks tracks between between railway railway companies, companies, introducing introducing platform platform screen screen doors doors and and so so on. on. Operating Operating trains trains with with high high frequency frequency and and sharing sharing tracks tracks between between railway railway companies companies have have been been the the key key policies policies in in Japan Japan to to reduce reduce train train congestion. congestion.
2214-241X 2214-241X © © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. Peer-review Peer-review under under responsibility responsibility of of WORLD WORLD CONFERENCE CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 10.1016/j.trpro.2017.05.313
HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 4375 2 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio/ Transportation Research Procedia 00 (2017) 000–000
However these measures have made the railway network in TMA extremely complex. Once there occurs a trouble, this makes the complex train operation disordered and the service level is decreased tremendously and widely for a long time. In TMA, railway accidents and transportation obstruction have been increasing. The number of the cases is 1,129 in the fiscal 2012. It means that 3.1 troubles occur per day. The influence of the troubles on the society is increasing. Therefore the measures against the troubles are placed as an important theme among the transport policies about the railway in TMA. When a disordered operation is brought about, rescheduling methods are operated. However the methods are combined complicatedly almost depending on the experiences of each dispatcher, thus the most suitable combination of the methods has not been established. The objective of the study is to visualize the train operation when train traffic is disrupted on the basis of the actual data. The study illustrates the several cases when trains are seriously delayed by applying the actual result operation data in order to highlight the difference caused by the factors such as circumstances the companies, situations of the accident and railway line, duration of accident and so on. There exist a lot of study results concerning train operation when train traffic is disrupted. Almost all studies develop simulation systems to improve train operation under disordered situation. However, there has been little study about grasping the actual delay under disordered operation based on the actual data. In the study, several actual cases are analyzed to illustrate the actual delay under disordered operation. Therefore the study is ranked as the demonstrative study to be a help to the transportation policy. 2. DATA PROFILES AND DEFINITION OF TERMS 2-1. DATA PROFILES There are three kinds of data in the study: data about train accident, train operation record data and delay certificates. Each feature of the data is shown as follows: Firstly, “the data about train accident” is composed of railway accident (railway accident with casualty, level crossing accident, train collision accident, derailment, train fire accident, railway damage accident and road obstruction accident) and transportation obstruction (except for railway accident) such as suspension, more than 30 minutes delay of passenger trains and more than 60 minutes delay of freight trains (Figure 1). The data are reported to the government by each railway company. The data are composed of date, time and place, cause, the number of suspended train, the number of delayed train and maximum delay of accident. Secondly, “train operation record data” are the data which recorded actual arrival and departure times of each train at each station by the train operation control system. Thirdly, “delay certificates” are the data which recorded the maximum delay rounded up to the next 5 or 10 minutes in each line and period of time. The data is disclosed on the web to certify the train delay officially by each railway company. In TMA, passengers that have tickets by way of the accidental line are able to get on the alternative lines without other tickets. 2-2. DEFINITION OF TERMS The study lists up the definitions of the terms in the following chapters: ”Accident with casualty” is defined as a railway accident which happened due to an intrusion into the track, falling from a platform or suicide (Figure 1). “Arrival delay” is defined as the difference (actual arrival time – planned arrival time at a station). “Departure delay” is defined as the difference (actual departure time – planned departure time at a station). “Delay” means “arrival delay” and “departure delay”. “Delay caused by an increase of the dwell time (hereinafter DID)” is defined as the difference (actual dwell time – planned dwell time at a station). “Delay caused by an increase of the running time (hereinafter DIR)” is defined as the difference (actual running time – planned running time between some stations). “Increase of delay” is defined as DID and DIR. “Maximum delay” is defined as the maximum “delay” in a whole line. “Time when the operation is resumed (hereinafter TRS)” is defined as the time when the train
4376 HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio / Transportation Research Procedia 00 (2017) 000–0003
having had an accident at the accident point resumes operation. “Time when normal operation is recovered (herein after TRC)” is defined as the time when the last over 20 minutes delayed train in a whole line arrive at the terminal station. “Train graph” is defined as the diagram with the horizontal axis as the time and the vertical axis as stations. The diagram is composed of polygonal lines connecting each departure time and arrival time at each station collectively. “Train path” is defined as a polygonal line describing a train motion in the “train graph.” “Train path replacement” is defined as the suspending the tremendously delayed train path to allocate the train sets to the non-delayed train path in order to vanish the tremendous delay. 3. STRUCTURE OF THE STUDY With various data, the study comprehends the overview and the details about disordered operations at the time of accidents with casualty. Firstly, to comprehend an overview, the study interviews 9 railway companies having lines in TMA from the viewpoint of the rescheduling methods. In addition, the study analyzed the data of train accident of those companies from the viewpoint of the number of accidents with casualty and the maximum delays. Therefore the study chooses a characteristic company through the overview. Moreover, in order to grasp the details, the study chooses a specific line that is easy to comprehend the distinction between normal operation and disordered operation at the time of accidents with casualty. Secondly, the study analyzes the train operation record data of the line under disordered operation to clarify how the trains are operated. In addition, the study analyzes the delay certificates of the line and lines close to the line to clarify the difference in delay because of the influx of the passengers in the alternative transportation from the line. 4. COMPREHENDING THE OVERVIEW 4-1. INTERVIEW SURVEY The study interviews 19 transportation staff of 9 companies (hereinafter A-I) about the rescheduling methods at the time of accident with casualty. About the operation management, each method has been already proposed minutely in the research in the past. These methods are used in combination depending on the complexity of the cases. The choice of the methods depends on the cultures of companies, the number of trains, the locations of depots, the number of personnel and the combination of railway facilities. Under this situation, the study asked three questions: how to control the routes (Q1), how to resume operation quickly (Q2) and how to recover from the disordered operation to the normal operation (Q3). Specifically, about Q1, whether the route controls are operated under usual operation (1.1) and disordered operation (1.2) automatically or manually is questioned. About Q2, whether there is a possibility or less possibility of the following two methods are operated is questioned: The first method is resuming operation immediately before the investigation by Police after the clearance limit inspection (2.1). The second method is continuing operation while Police investigating (2.2). About Q3, whether the following four combinations of methods are operated or not is questioned: The first combination is as follows: replacing the tremendously delayed train path at the intermediate station with the non-delayed train path (3.1), the paths are in the same traveling direction (3.2), after the replacement, the former path gets partially suspended (3.3) and the train set of the former and latter paths are the same (3.4). The second combination is as follows: (3.1), (3.2), (3.3) and the train set of the former and the latter paths are not the same. The third combination is as follows: replacing the tremendously delayed train path at the starting station with non-delayed train path (3.5), the paths are in the opposite traveling direction (3.6), and (3.3). The fourth combination is as follows: (3.5), (3.6) and after the replacement, the former path gets suspended (3.7).
HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 4377 4 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio/ Transportation Research Procedia 00 (2017) 000–000
Fig.1. targeted cases
Figure2 shows the survey result. It shows the complex combination of methods among each company. Firstly, the route controls are categorized into three groups. The feature of the first group (A, B, C, E, G) is that the route controls are operated automatically under usual and disordered operation. The feature of the second group (D, F, I) is that the route controls are also operated automatically under normal operation, however the manual operations are introduced to route controls under disordered operation. The feature of the third group (H) is that the route control is operated manually under usual and disordered operation. Secondly, quick resuming operations are categorized into three groups. The features of the first group (A, C, D, H, I) is that (2.1) and (2.2) are operated. The feature of the second group (B and F) is that (2.1) is not operated, however (2.2) is operated. The feature of the third group (E and G) is that (2.1) and (2.2) are not operated. Thirdly, recovery to planned operation is categorized into three groups. Both the combinations of (3.6), (3.7) and (3.8) and the combinations of (3.6), (3.7) and (3.3) are operated by each company. The rest of the methods are categorized into three groups. The feature of the first group (A, D, E, F, H and I) is that both the combination of (3.1), (3.2), (3.3) and (3.4) and the combination of (3.1), (3.2), (3.3) and (3.5) are operated. The feature of the second group (C) is that the former combination is not operated however the latter combination is operated. The feature of the third group is that neither the former combination nor the latter combination are not operated.
HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio / Transportation Research Procedia 00 (2017) 000–0005 4378 HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384
Fig.2. cases of operation management
4-2. TRAIN ACCIDENTS DATA ANALYSIS The data of train accident whose causes are accidents with casualty of each company and the fiscal 2008-2012 are used. The data are operated as follows to compare the various data based on the different train travel distance. Firstly, the number of accidents is totaled of the fiscal years and the causes by the companies (=α-1). Similarly, the maximum delay is totaled of the fiscal years and the causes by the companies (=β-1). Secondly, each “α-1” is divided by the total train travel distance of the fiscal years (=α-2). Similarly, each “β-1” is divided by total train travel distance of the fiscal year (=β-2). Thus “α2” is compared with “β-2” by the fiscal years and the companies. Figure3 shows the relation between “α-2” and “β-2” by the companies and the fiscal years. Figure3 shows the features as follows: The first feature is that all point of H and I is almost scattered near the origin, along with the approximate curve. However all point of B and C varies widely so as not to be scattered along with the approximate curve. The second feature is that the five points of A are scattered closed to each other, along with the approximate curve. The third feature is that the degree of leaning of the approximate curve is about 71 minutes per case.
6 HIBINO Naohiko, NAGAOKA Osamu, HIBINO Naohiko et al.IEDA / Transportation Research 25C (2017) 4374–4384 4379 MORICHI Shigeru, Hitoshi, Tomii Norio/Procedia Transportation Research Procedia 00 (2017) 000–000
Fig.3. relation between “α-2” and β-2
5. COMPREHENDING THE DETAIL 5-1. TRAIN OPERATION RECORD DATA ANALYSIS The train operation record data of “A” company that clarifies following three characteristics in the chapter 4 is analyzed. Firstly, “A” company replies that the route control is operated by computer under both normal operation and disordered operation. Secondly, “A” company also replies that there is possibility of all rescheduling method is operated. Thirdly, in the Figure3, there is a little annual fluctuation of “A” company, and each point is scattered close to the approximately curve. Among the lines in the “A” company, a line is targeted that is operated under parallel diagram and a distance between stations is relatively longer in the lines connecting suburbs to city center. The data of days when accidents with casualty occur in the fiscal 2012 is targeted. Two graphs are developed in order to comprehend the train operation result data roughly or minutely. About the first diagram, called Rough Diagram (hereinafter RD), both 3D and 2D graphs are developed. The features of the graphs are setting of the axes. The axes of the 3D graph are traveling direction, the departure time from the first station and delay. The axes of the 2D graph are traveling direction and delay. About the second diagram, called Detailed Diagram (hereinafter DD), there already is the Chromatic Diagram visualizing the small delay occurring almost everyday in a past study. Based on the Chromatic Diagram, Figure4 shows the method of visualizing the disordered operation because of accidents with casualty. In DD, delay caused by increase of running time and delay caused by increase of dwell time are shown with size of bubbles visually and the delay when a train arrive at or depart from a station is shown with value. To avoid the excessive overlapping, the inbound lane and the outbound lane are divided. It is clarified that how the bubbles showing the increase of delay are scattered in the diagram and the period of time when the accumulated delay because of the suspension.
4380 HIBINO Naohiko et al.IEDA / Transportation Research 25C (2017) 4374–4384 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, Hitoshi, Tomii Norio /Procedia Transportation Research Procedia 00 (2017) 000–0007
Fig.4. the method of visualizing the disordered operation
Figure5 shows the relation between TRS and TRC. The TRS is about one hour regardless of the period of time. However, comparing the TRC of four cases (day1, day2, day3 and day4) occurs from 7 to 9 with cases that occur in the other period of time, the former cases are larger than the latter cases. Based on the results, the case at day1 is focused.
Fig.5. the relation between TRS and TRC
HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 4381 8 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio/ Transportation Research Procedia 00 (2017) 000–000
Figure6 shows the result of adapting the case at day1 to RD in 3D. The Graph shows the little fluctuation (between 0 and 2 minutes) under normal operation. However, under disordered operation, the delay increases from the time when an accident occurs to the TRS and the delay is not dissolved till arriving at the terminal station after the TRS. Therefore in order to comprehend the increases of delay from each TRS, Figure7 shows the result of adapting the case at day1 to RD in 2D. It shows two types of fluctuations. The first type has a little fluctuation. The second type has large fluctuations (A, B, C, D, E and F). In order to clarify the causes of them, the case at day1 is adapted to DD. In the DD, each DIR and DID is splattered as bubbles. Figure8 shows the 6 large fluctuations (A, B, C, D, E and F). To comprehend the causes of the fluctuations more precisely, the fluctuations are magnified in the Figure9. In the area A, the causes of the delay is that the train from the former station is not able to start until the first train starts at the station. The causes of the delay in the area B, C, D, E and F are that the train from the former station is not able to start until the dwell of the train ends.
Fig.6. the RD in 3D
Fig.7. RD in 2D
Fig.8. DD
4382 HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio / Transportation Research Procedia 00 (2017) 000–0009
Fig.9. fluctuations magnified in the DD 5-2. DELAY CERTIFICATE ANALYSIS The delay described in the certificates is analyzed. The data comprises 3 companies, 5 lines, for 252 days except for Saturday, Sunday and National holiday in the fiscal 2012. The lines have same characteristics. Firstly, the lines are connecting the city center and the suburbs. Secondly, because of the lines are close to each other, on the occasion of accident with casualty occurs in a line, the other lines are announced as the alternative transportation to passengers by each company immediately. The data of the four days (day1, day2, day3 and day4) when accidents with casualty occurred referred in the chapter 4 is compared with each other. Furthermore, except for the data over 60 minutes, the averages are calculated of each maximum and minimum value that are estimated the actual delay. Calculated averages are compared with the delay described.
HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 4383 10HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio/ Transportation Research Procedia 00 (2017) 000–000
Figure10 shows the calculated averages and the delay described of the four days. Focusing on the relation among the lines, on day3 and day4, the value of the delays described of the line1 and line2 of the other company are smaller than the line1 and line2 of the same company. On the other hand, on day1 and day2, the values of the delays described of the line1 and line2 of the same company are smaller than the line1 of the other company. Therefore which line has the maximum delay depends on occasion. Moreover focusing on the scale of the value of delays described, more than 60 minutes delay occurs on the same line of the same company. On the other hand, around 20 minutes delay occurs on the line of the other company. Furthermore focusing on the averages and the value of delays described of the other companies, on all the days, the value of the delays is larger than the averages on the line1 of the other company. On the day2 and day4, the values of the delays described are larger than the averages.
Fig.10. analysis of delay certificate
6. CONCLUSION Through the comprehending the overview and the details, the study clarifies the following facts about the methods against the railway accidents and the transportation obstructions in TMA. The interview survey shows that there are differences in the following measures from one company to another: route controls, quick resuming operations and recovery to normal operation. Therefore, applying a specific method which is used in a specific company by another company does not work effectively. A comprehensive consideration is important including the following background: the cultures of companies, the number of trains, the locations of depots, the number of personnel and the combination of railway facilities. The analysis of train accidents data shows that there is a strong correlation between “α-3” and “β-3” by the companies and the fiscal years. Therefore the result shows that the analysis is helpful for the discussion about reduction of delays over each company. The analysis of train operation record data shows the difference of the delay fluctuations between the normal operation and the disordered operation by illustrating the data in the two visual graphs. Moreover the analysis shows the method to estimate the cause of the increasing delay after TRS by combining the two graphs. Therefore the analysis signifies the possibility to make use of the basic data under disordered operations.
4384 HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio / Transportation Research Procedia 00 (2017) 000–00011
The analysis of delay certificates shows that as to lines close to the targeted line where an accident occurs, the delay described on the certificates is larger than the average throughout the fiscal year. The analysis also shows the following two estimated causes of the delays. Firstly as to the lines of the same company, the cause is stopping the trains in order to make the area safe where accident occurs. Secondly, as to the lines of other companies, the cause is the congestion because of the acceptance of the alternative transportation. Moreover the analysis shows the distinction among the lines close to the targeted line. Thus if there are several alternative transportations, the information is important that considers the capacity of the transportation of each line. Through the clarification of each fact, the study shows the importance of the comparative analysis with various data as a basis of the measures against the railway accidents and transportation obstructions that is ranked as one of the important and pressing issues of the transport policy in TMA. ACKNOWLEDGEMENTS The authors greatly appreciate the help of the nine railway companies in TMA. In addition, the authors appreciate Professor Hajime Inamura and Professor Satoshi Inoue who gave us useful comments in the seminar. This research was supported by the Japan Society for the Promotion of Science (JSPS) under the Grants-in-Aid Scientific Research 10318206.
REFERENCES Committee for Advanced Train Scheduling and Rescheduling of Institute of Electrical Engineering Japan: Techniques of Train Rescheduling, pp.1-212, Ohm Publishing Co. Ltd., 2010 (in Japanese). Christian, L., 2007, Recoverable Robustness, Technical Report of ARRIVAL-TR-0066. Kariyazaki. K. et al, 2011, Simulation Analysis of Daily Service Delay Focusing on Train Headway, Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), Vol.67, No.5, pp.I_1001-I_1010 (in Japanese). Kariyazaki. K. et al, 2013, Simulation Model for Estimating Train Operation to Recover Knock-on Delay Earlier, Asian Transport Studies, Vol.2 No.3, pp.284-294. Kariyazaki. K. et al, 2015, Simulation Analysis of Train Operation to Recover Knock-on Delay under High-Frequency Intervals, Case Studies on Transport Policy, Vol. 3, Issue 1, pp.92-98. Malachy, C., 1999, Ex ante heuristic measures of schedule reliability, Transportation Research Part B, Vol.33, pp.473-494. Ministry of Land, Infrastructure, Transport and Tourism, 2013, Occurrences of train accidents in the area of Kanto District Transport Bureau, https://wwwtb.mlit.go.jp/kanto/tetudou/tetudou_anzen/date/jikohassei_25_2.pdf (accessed 2014/7/25) (in Japanese). Miyazaki, K., et al, 2014, Analysis of Train Delay in Urban Railway Services Based on Characteristics of Each Line, Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management), Vol.70, No.5, pp.I_477-I_486 (in Japanese). Miyazaki, N., 2007, A methodology for estimating speed of urban railway using multi agent simulation, Proceedings of the 14th Jointed Railway Technology Symposium, pp.365-368 (in Japanese). Nagel, K., 1992, A cellular automaton model for freeway traffic, Journal de Physique I France 2, pp.2221-2229. Saishu, H., 2013, A Study on Dealing with Service Interruption of Urban Railways, Transport Policy Studies’ Review, Vol.16, No.2, pp.96-99 (in Japanese). Tsuchiya, R., 2008, Route Choice Support System for Passengers during Disruption of Train Operations and Its Acceptability Evaluation, 49(2), pp868-880 (in Japanese). Tsunoda, F., 2013, A Study for Quantification Method of Passenger's Influence by Railway Accident with Transport IC Card, Transactions of Information Processing Society of Japan, Database 6(3) , pp.187-196 (in Japanese). Ushida, K., 2010, Application of Visualization Methods to Timetable Analysis, Transactions of Japan Train Operation Association, Vol.52, No.8 (in Japanese). Yamamura, A., 2012, Delay Reduction Measures in Dense Transportation Operation, Proceedings of the J-Rail 2012, pp.381-384 (in Japanese).
ScienceDirect
Available online at www.sciencedirect.com
Transportation Transportation Research Research Procedia Procedia 00 00 (2017) (2017) 000–000 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Transportation Research Procedia 25C (2017) 4374–4384 www.elsevier.com/locate/procedia
World Conference on Transport Research - WCTR 2016 Shanghai. 10-15 July 2016
Recovery measure of disruption in train operation in Tokyo Metropolitan Area HIBINO Naohikoaa, NAGAOKA Osamuaa, MORICHI Shigeruaa, IEDA Hitoshiaa, Tomii Noriobb a a
National 7-22-1 Roppongi, National Graduate Graduate Institute Institute for for Policy Policy Studies, Studies, 7-22-1 Roppongi, Minato-ku, Minato-ku, Tokyo Tokyo 106-8677, 106-8677, Japan Japan b bChiba Intsitute of Techonology, 2-17-1, Tsudanuma, Narashino-shi Chiba 275-0016, Japan Chiba Intsitute of Techonology, 2-17-1, Tsudanuma, Narashino-shi Chiba 275-0016, Japan
Abstract Abstract
In Metropolitan Area, Area, there there is is one one of of the the densest densest railway railway networks networks in in the the world. world. Passengers Passengers are are provided provided with with In Tokyo Tokyo Metropolitan the high high level level services services such such as as mutual mutual direct direct and and high high frequent frequent train train operation. operation. The The passengers, passengers, however, however, suffer suffer from from the the negative negative impact impact such such as as inconvenience inconvenience when when train train traffic traffic is is disrupted disrupted by by an an accident. accident. In In particular, particular, during during peak peak the hours, they have to commute in a congested train for long time. This is a kind of paradox and a serious social hours, they have to commute in a congested train for long time. This is a kind of paradox and a serious social problem which which should should be be solved solved immediately. immediately. The The objective objective of of this this study study is is to to visualize visualize the the results results of of train train operation operation problem when when train train traffic traffic is is disrupted disrupted on on the the basis basis of of the the actual actual data. data. The The study study illustrates illustrates the the several several cases cases when when trains trains are are seriously seriously delayed delayed by by using using actual actual result result operation operation data data in in order order to to analyse analyse the the difference difference caused caused by by the the factors factors such such as as circumstances circumstances of of the the companies, companies, situations situations of of the the accident accident and and railway railway line, line, duration duration of of accident accident and and so so on. on.
© © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. Peer-review under responsibility of WORLD CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY. Peer-review under responsibility of WORLD CONFERENCE Keywords: Keywords: train train delay, delay, interview interview survey, survey, train train operation operation record record data, data, delay delay certificates, certificates, visualization visualization
1. Introduction 1. Introduction In Tokyo Tokyo Metropolitan Metropolitan Area Area (hereinafter (hereinafter TMA), TMA), aa huge huge number number of of passengers passengers are are transferred transferred by by the the railway railway In network everyday network everyday over over aa wide wide area. area. Today, Today, TMA’s TMA’s railway railway network network is is known known as as one one of of the the world’s world’s leading leading transport transport systems with reliable, reliable, safe safe and and convenient convenient operation. operation. Service Service quality quality of of the the systems in in handling handling aa huge huge traffic traffic volume volume with network developing aa high high network has has been been improved improved constantly constantly through through the the following following measures: measures: using using long long train-sets, train-sets, developing density density railway railway network, network, operating operating trains trains with with aa high high frequency, frequency, sharing sharing tracks tracks between between railway railway companies, companies, introducing introducing platform platform screen screen doors doors and and so so on. on. Operating Operating trains trains with with high high frequency frequency and and sharing sharing tracks tracks between between railway railway companies companies have have been been the the key key policies policies in in Japan Japan to to reduce reduce train train congestion. congestion.
2214-241X 2214-241X © © 2017 2017 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. Peer-review Peer-review under under responsibility responsibility of of WORLD WORLD CONFERENCE CONFERENCE ON ON TRANSPORT TRANSPORT RESEARCH RESEARCH SOCIETY. SOCIETY.
2352-1465 © 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of WORLD CONFERENCE ON TRANSPORT RESEARCH SOCIETY. 10.1016/j.trpro.2017.05.313
HIBINO Naohiko et al. / Transportation Research Procedia 25C (2017) 4374–4384 4375 2 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, IEDA Hitoshi, Tomii Norio/ Transportation Research Procedia 00 (2017) 000–000
However these measures have made the railway network in TMA extremely complex. Once there occurs a trouble, this makes the complex train operation disordered and the service level is decreased tremendously and widely for a long time. In TMA, railway accidents and transportation obstruction have been increasing. The number of the cases is 1,129 in the fiscal 2012. It means that 3.1 troubles occur per day. The influence of the troubles on the society is increasing. Therefore the measures against the troubles are placed as an important theme among the transport policies about the railway in TMA. When a disordered operation is brought about, rescheduling methods are operated. However the methods are combined complicatedly almost depending on the experiences of each dispatcher, thus the most suitable combination of the methods has not been established. The objective of the study is to visualize the train operation when train traffic is disrupted on the basis of the actual data. The study illustrates the several cases when trains are seriously delayed by applying the actual result operation data in order to highlight the difference caused by the factors such as circumstances the companies, situations of the accident and railway line, duration of accident and so on. There exist a lot of study results concerning train operation when train traffic is disrupted. Almost all studies develop simulation systems to improve train operation under disordered situation. However, there has been little study about grasping the actual delay under disordered operation based on the actual data. In the study, several actual cases are analyzed to illustrate the actual delay under disordered operation. Therefore the study is ranked as the demonstrative study to be a help to the transportation policy. 2. DATA PROFILES AND DEFINITION OF TERMS 2-1. DATA PROFILES There are three kinds of data in the study: data about train accident, train operation record data and delay certificates. Each feature of the data is shown as follows: Firstly, “the data about train accident” is composed of railway accident (railway accident with casualty, level crossing accident, train collision accident, derailment, train fire accident, railway damage accident and road obstruction accident) and transportation obstruction (except for railway accident) such as suspension, more than 30 minutes delay of passenger trains and more than 60 minutes delay of freight trains (Figure 1). The data are reported to the government by each railway company. The data are composed of date, time and place, cause, the number of suspended train, the number of delayed train and maximum delay of accident. Secondly, “train operation record data” are the data which recorded actual arrival and departure times of each train at each station by the train operation control system. Thirdly, “delay certificates” are the data which recorded the maximum delay rounded up to the next 5 or 10 minutes in each line and period of time. The data is disclosed on the web to certify the train delay officially by each railway company. In TMA, passengers that have tickets by way of the accidental line are able to get on the alternative lines without other tickets. 2-2. DEFINITION OF TERMS The study lists up the definitions of the terms in the following chapters: ”Accident with casualty” is defined as a railway accident which happened due to an intrusion into the track, falling from a platform or suicide (Figure 1). “Arrival delay” is defined as the difference (actual arrival time – planned arrival time at a station). “Departure delay” is defined as the difference (actual departure time – planned departure time at a station). “Delay” means “arrival delay” and “departure delay”. “Delay caused by an increase of the dwell time (hereinafter DID)” is defined as the difference (actual dwell time – planned dwell time at a station). “Delay caused by an increase of the running time (hereinafter DIR)” is defined as the difference (actual running time – planned running time between some stations). “Increase of delay” is defined as DID and DIR. “Maximum delay” is defined as the maximum “delay” in a whole line. “Time when the operation is resumed (hereinafter TRS)” is defined as the time when the train
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having had an accident at the accident point resumes operation. “Time when normal operation is recovered (herein after TRC)” is defined as the time when the last over 20 minutes delayed train in a whole line arrive at the terminal station. “Train graph” is defined as the diagram with the horizontal axis as the time and the vertical axis as stations. The diagram is composed of polygonal lines connecting each departure time and arrival time at each station collectively. “Train path” is defined as a polygonal line describing a train motion in the “train graph.” “Train path replacement” is defined as the suspending the tremendously delayed train path to allocate the train sets to the non-delayed train path in order to vanish the tremendous delay. 3. STRUCTURE OF THE STUDY With various data, the study comprehends the overview and the details about disordered operations at the time of accidents with casualty. Firstly, to comprehend an overview, the study interviews 9 railway companies having lines in TMA from the viewpoint of the rescheduling methods. In addition, the study analyzed the data of train accident of those companies from the viewpoint of the number of accidents with casualty and the maximum delays. Therefore the study chooses a characteristic company through the overview. Moreover, in order to grasp the details, the study chooses a specific line that is easy to comprehend the distinction between normal operation and disordered operation at the time of accidents with casualty. Secondly, the study analyzes the train operation record data of the line under disordered operation to clarify how the trains are operated. In addition, the study analyzes the delay certificates of the line and lines close to the line to clarify the difference in delay because of the influx of the passengers in the alternative transportation from the line. 4. COMPREHENDING THE OVERVIEW 4-1. INTERVIEW SURVEY The study interviews 19 transportation staff of 9 companies (hereinafter A-I) about the rescheduling methods at the time of accident with casualty. About the operation management, each method has been already proposed minutely in the research in the past. These methods are used in combination depending on the complexity of the cases. The choice of the methods depends on the cultures of companies, the number of trains, the locations of depots, the number of personnel and the combination of railway facilities. Under this situation, the study asked three questions: how to control the routes (Q1), how to resume operation quickly (Q2) and how to recover from the disordered operation to the normal operation (Q3). Specifically, about Q1, whether the route controls are operated under usual operation (1.1) and disordered operation (1.2) automatically or manually is questioned. About Q2, whether there is a possibility or less possibility of the following two methods are operated is questioned: The first method is resuming operation immediately before the investigation by Police after the clearance limit inspection (2.1). The second method is continuing operation while Police investigating (2.2). About Q3, whether the following four combinations of methods are operated or not is questioned: The first combination is as follows: replacing the tremendously delayed train path at the intermediate station with the non-delayed train path (3.1), the paths are in the same traveling direction (3.2), after the replacement, the former path gets partially suspended (3.3) and the train set of the former and latter paths are the same (3.4). The second combination is as follows: (3.1), (3.2), (3.3) and the train set of the former and the latter paths are not the same. The third combination is as follows: replacing the tremendously delayed train path at the starting station with non-delayed train path (3.5), the paths are in the opposite traveling direction (3.6), and (3.3). The fourth combination is as follows: (3.5), (3.6) and after the replacement, the former path gets suspended (3.7).
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Fig.1. targeted cases
Figure2 shows the survey result. It shows the complex combination of methods among each company. Firstly, the route controls are categorized into three groups. The feature of the first group (A, B, C, E, G) is that the route controls are operated automatically under usual and disordered operation. The feature of the second group (D, F, I) is that the route controls are also operated automatically under normal operation, however the manual operations are introduced to route controls under disordered operation. The feature of the third group (H) is that the route control is operated manually under usual and disordered operation. Secondly, quick resuming operations are categorized into three groups. The features of the first group (A, C, D, H, I) is that (2.1) and (2.2) are operated. The feature of the second group (B and F) is that (2.1) is not operated, however (2.2) is operated. The feature of the third group (E and G) is that (2.1) and (2.2) are not operated. Thirdly, recovery to planned operation is categorized into three groups. Both the combinations of (3.6), (3.7) and (3.8) and the combinations of (3.6), (3.7) and (3.3) are operated by each company. The rest of the methods are categorized into three groups. The feature of the first group (A, D, E, F, H and I) is that both the combination of (3.1), (3.2), (3.3) and (3.4) and the combination of (3.1), (3.2), (3.3) and (3.5) are operated. The feature of the second group (C) is that the former combination is not operated however the latter combination is operated. The feature of the third group is that neither the former combination nor the latter combination are not operated.
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Fig.2. cases of operation management
4-2. TRAIN ACCIDENTS DATA ANALYSIS The data of train accident whose causes are accidents with casualty of each company and the fiscal 2008-2012 are used. The data are operated as follows to compare the various data based on the different train travel distance. Firstly, the number of accidents is totaled of the fiscal years and the causes by the companies (=α-1). Similarly, the maximum delay is totaled of the fiscal years and the causes by the companies (=β-1). Secondly, each “α-1” is divided by the total train travel distance of the fiscal years (=α-2). Similarly, each “β-1” is divided by total train travel distance of the fiscal year (=β-2). Thus “α2” is compared with “β-2” by the fiscal years and the companies. Figure3 shows the relation between “α-2” and “β-2” by the companies and the fiscal years. Figure3 shows the features as follows: The first feature is that all point of H and I is almost scattered near the origin, along with the approximate curve. However all point of B and C varies widely so as not to be scattered along with the approximate curve. The second feature is that the five points of A are scattered closed to each other, along with the approximate curve. The third feature is that the degree of leaning of the approximate curve is about 71 minutes per case.
6 HIBINO Naohiko, NAGAOKA Osamu, HIBINO Naohiko et al.IEDA / Transportation Research 25C (2017) 4374–4384 4379 MORICHI Shigeru, Hitoshi, Tomii Norio/Procedia Transportation Research Procedia 00 (2017) 000–000
Fig.3. relation between “α-2” and β-2
5. COMPREHENDING THE DETAIL 5-1. TRAIN OPERATION RECORD DATA ANALYSIS The train operation record data of “A” company that clarifies following three characteristics in the chapter 4 is analyzed. Firstly, “A” company replies that the route control is operated by computer under both normal operation and disordered operation. Secondly, “A” company also replies that there is possibility of all rescheduling method is operated. Thirdly, in the Figure3, there is a little annual fluctuation of “A” company, and each point is scattered close to the approximately curve. Among the lines in the “A” company, a line is targeted that is operated under parallel diagram and a distance between stations is relatively longer in the lines connecting suburbs to city center. The data of days when accidents with casualty occur in the fiscal 2012 is targeted. Two graphs are developed in order to comprehend the train operation result data roughly or minutely. About the first diagram, called Rough Diagram (hereinafter RD), both 3D and 2D graphs are developed. The features of the graphs are setting of the axes. The axes of the 3D graph are traveling direction, the departure time from the first station and delay. The axes of the 2D graph are traveling direction and delay. About the second diagram, called Detailed Diagram (hereinafter DD), there already is the Chromatic Diagram visualizing the small delay occurring almost everyday in a past study. Based on the Chromatic Diagram, Figure4 shows the method of visualizing the disordered operation because of accidents with casualty. In DD, delay caused by increase of running time and delay caused by increase of dwell time are shown with size of bubbles visually and the delay when a train arrive at or depart from a station is shown with value. To avoid the excessive overlapping, the inbound lane and the outbound lane are divided. It is clarified that how the bubbles showing the increase of delay are scattered in the diagram and the period of time when the accumulated delay because of the suspension.
4380 HIBINO Naohiko et al.IEDA / Transportation Research 25C (2017) 4374–4384 HIBINO Naohiko, NAGAOKA Osamu, MORICHI Shigeru, Hitoshi, Tomii Norio /Procedia Transportation Research Procedia 00 (2017) 000–0007
Fig.4. the method of visualizing the disordered operation
Figure5 shows the relation between TRS and TRC. The TRS is about one hour regardless of the period of time. However, comparing the TRC of four cases (day1, day2, day3 and day4) occurs from 7 to 9 with cases that occur in the other period of time, the former cases are larger than the latter cases. Based on the results, the case at day1 is focused.
Fig.5. the relation between TRS and TRC
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Figure6 shows the result of adapting the case at day1 to RD in 3D. The Graph shows the little fluctuation (between 0 and 2 minutes) under normal operation. However, under disordered operation, the delay increases from the time when an accident occurs to the TRS and the delay is not dissolved till arriving at the terminal station after the TRS. Therefore in order to comprehend the increases of delay from each TRS, Figure7 shows the result of adapting the case at day1 to RD in 2D. It shows two types of fluctuations. The first type has a little fluctuation. The second type has large fluctuations (A, B, C, D, E and F). In order to clarify the causes of them, the case at day1 is adapted to DD. In the DD, each DIR and DID is splattered as bubbles. Figure8 shows the 6 large fluctuations (A, B, C, D, E and F). To comprehend the causes of the fluctuations more precisely, the fluctuations are magnified in the Figure9. In the area A, the causes of the delay is that the train from the former station is not able to start until the first train starts at the station. The causes of the delay in the area B, C, D, E and F are that the train from the former station is not able to start until the dwell of the train ends.
Fig.6. the RD in 3D
Fig.7. RD in 2D
Fig.8. DD
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Fig.9. fluctuations magnified in the DD 5-2. DELAY CERTIFICATE ANALYSIS The delay described in the certificates is analyzed. The data comprises 3 companies, 5 lines, for 252 days except for Saturday, Sunday and National holiday in the fiscal 2012. The lines have same characteristics. Firstly, the lines are connecting the city center and the suburbs. Secondly, because of the lines are close to each other, on the occasion of accident with casualty occurs in a line, the other lines are announced as the alternative transportation to passengers by each company immediately. The data of the four days (day1, day2, day3 and day4) when accidents with casualty occurred referred in the chapter 4 is compared with each other. Furthermore, except for the data over 60 minutes, the averages are calculated of each maximum and minimum value that are estimated the actual delay. Calculated averages are compared with the delay described.
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Figure10 shows the calculated averages and the delay described of the four days. Focusing on the relation among the lines, on day3 and day4, the value of the delays described of the line1 and line2 of the other company are smaller than the line1 and line2 of the same company. On the other hand, on day1 and day2, the values of the delays described of the line1 and line2 of the same company are smaller than the line1 of the other company. Therefore which line has the maximum delay depends on occasion. Moreover focusing on the scale of the value of delays described, more than 60 minutes delay occurs on the same line of the same company. On the other hand, around 20 minutes delay occurs on the line of the other company. Furthermore focusing on the averages and the value of delays described of the other companies, on all the days, the value of the delays is larger than the averages on the line1 of the other company. On the day2 and day4, the values of the delays described are larger than the averages.
Fig.10. analysis of delay certificate
6. CONCLUSION Through the comprehending the overview and the details, the study clarifies the following facts about the methods against the railway accidents and the transportation obstructions in TMA. The interview survey shows that there are differences in the following measures from one company to another: route controls, quick resuming operations and recovery to normal operation. Therefore, applying a specific method which is used in a specific company by another company does not work effectively. A comprehensive consideration is important including the following background: the cultures of companies, the number of trains, the locations of depots, the number of personnel and the combination of railway facilities. The analysis of train accidents data shows that there is a strong correlation between “α-3” and “β-3” by the companies and the fiscal years. Therefore the result shows that the analysis is helpful for the discussion about reduction of delays over each company. The analysis of train operation record data shows the difference of the delay fluctuations between the normal operation and the disordered operation by illustrating the data in the two visual graphs. Moreover the analysis shows the method to estimate the cause of the increasing delay after TRS by combining the two graphs. Therefore the analysis signifies the possibility to make use of the basic data under disordered operations.
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The analysis of delay certificates shows that as to lines close to the targeted line where an accident occurs, the delay described on the certificates is larger than the average throughout the fiscal year. The analysis also shows the following two estimated causes of the delays. Firstly as to the lines of the same company, the cause is stopping the trains in order to make the area safe where accident occurs. Secondly, as to the lines of other companies, the cause is the congestion because of the acceptance of the alternative transportation. Moreover the analysis shows the distinction among the lines close to the targeted line. Thus if there are several alternative transportations, the information is important that considers the capacity of the transportation of each line. Through the clarification of each fact, the study shows the importance of the comparative analysis with various data as a basis of the measures against the railway accidents and transportation obstructions that is ranked as one of the important and pressing issues of the transport policy in TMA. ACKNOWLEDGEMENTS The authors greatly appreciate the help of the nine railway companies in TMA. In addition, the authors appreciate Professor Hajime Inamura and Professor Satoshi Inoue who gave us useful comments in the seminar. This research was supported by the Japan Society for the Promotion of Science (JSPS) under the Grants-in-Aid Scientific Research 10318206.
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