A Study on Train Travel Time Simulation focused on
detailed Dwell Time Structure and On-Site Inspections
Shigeaki Adachi a,1, Masahito Koresawa b, Giancarlos Troncoso Paradya, Kiyoshi Takami a, Noboru Harata aaDepartment of Urban Engineering, The University of Tokyo
Engineering building 14, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 Japan1
E-mail: adachi@ut.t.u-tokyo.ac.jp, Phone: +81 (3) 3837-7318
b
Department of Operation, Tokyo Metro Co., Ltd.
3-19-6 Higashi-ueno, Taito-ku, Tokyo 110-8614 Japan

Abstract
In order to reduce passenger congestion during morning rush hour, railway companies in
the Tokyo metropolitan area have increased the number of trains. On the other hand, oncea train exceeds a dwell time due to sudden events such as passengers rushing onto a train,passengers agglomerating in specific cars and doors, objects getting caught in doors etc.,
delays propagate to subsequent trains quickly. To evaluate daily train transport stability andcountermeasures against train delays, a train travel time simulation model is needed.
However, it has been difficult so far to replicate the occurrence of sudden events and thefluctuations in passenger demand. In this paper, we use detailed data based on dwell time
structure and on-site inspections to construct a train travel time simulator. In addition, we
evaluate several case-studies of timetable adjustments and passenger demand variations.
Keywords
Train delay, Train travel time simulation, Ticket gate ingress and egress record data,
Smirnoff-Grubbs test, Dwell time

1 Introduction
Railway companies in Tokyo metropolitan area of Japan have increased the number oftrains to alleviate passenger congestion and improve train delays during morning rush hour.
However, train headways are limited by the capacity of the signalling system. Under suchcircumstances, train delays propagate to subsequent trains because of short headways.
Furthermore, during boarding and alighting, when small sudden events such as passengers
rushing onto a train, passengers agglomerating in specific cars and doors, or objects gettingcaught in doors occur, dwell times are extended.
Train travel time simulation models have been constructed so far. Railway simulation
using traffic record data has been studied by Carey, M. (1999), Hürlimann, D. (2004), Van
der Meer, D. (2010), Graffagnino, T (2012). Furthermore, Hansen, I. et al (2014). havestudied various kinds of train simulators focusing on railway system functions. Janecek, D.
(2010) studied simulations focusing on changes in the infrastructure and timetable. Ushida,
K. et al. (2011) developed a chromatic diagram visualized reflecting train delays as colours.
In terms of train delay measures, Yamamura, A. (2013 & 2014) and Adachi, S. (2016) havestudied various kind of measures against train delays on one of the most congested lines inTokyo and evaluate those of effects on operation stability.

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 201915

However, these studies have mainly focused on railway system, simulator functions andtrain delay measures. . So far, it has been difficult to consider daily passenger demand andthe effect of small sudden events which occur frequently. Thus, consideration of thesedetailed elements is important to improve daily train operations. We have focused oncomposition of dwell time, and the relationship between passenger demand and dwell time
including such sudden events that has not been well studied so far.
In this paper, we construct a detailed train travel time simulation focusing on the Tokyo
Metro Tozai Line, which is one of the most congested lines in Tokyo.

2 Train diagram composition
Train head way is constructed by dwell time and minimum headway and buffer time. In adense timetable such as lines running in the city center, buffer times are set at almost
minimal, therefore once dwell time extends, buffer time becomes negative. This means thattrain delays propagate to subsequent trains.
Dwell time is segmented into 4 parts: passenger alighting time (A), passenger boarding
time (B), door closing confirmation time (C), and safety confirmation time (S). In terms ofdoor closing confirmation time (C), station staff judge timing of door closing at the end ofpassenger boarding. After passenger board, the staff give a signal to close doors to the
conductor, and the conductor close the doors. After door close, station staff confirm thesafety along cars and give a signal for departure to the conductor. This operation time isdefined as safety confirmation time (D). The most time-consuming door to alight and board
affects sum of passenger alighting time (A) and passenger boarding time (B).
Furthermore, it takes 2 seconds for doors to open after arriving at a station. According
to these definitions, dwell time at station i of train j is defined as (1). All times are given inseconds.
‫ܦ‬௜ǡ௝ ൌ ʹሺ‫ܿ݁ݏ‬ሻ ൅ ሺ‫ܣ‬௜ǡ௝ǡ௞ǡ௟ ൅ ‫ܤ‬௜ǡ௝ǡ௞ǡ௟ ሻ ൅ ‫ܥ‬௜ǡ௝ ൅ ܵ௜ǡ௝
௞ǡ௟

(1)

Ai, j, k, l: Alighting time at station i of train j, car No. k, door No. l
Bi, j, k, l: Boarding time at station i of train j, car No. k, door No. lCi, j: Closing confirmation time at station i of train j
Di, j: Dwell time at station i of train j
Si, j: Safety confirmation time at station i of train j

3 Factors influencing each time to construct dwell time
To build a detailed train travel time simulation, it is necessary to know what kind of factors
influence each time to construct dwell time. Factor affecting composed time are illustrated
in Figure 1. Alighting and boarding times are influenced by the number of passengers andby passenger congestion degree in a car. In Tozai line, some trains have wider door thanusual cars. This width also affects alighting and boarding times.
In terms of door closing operations, when staff judge the timing in some station, multiple
station staff members cooperate due to curved nature of some platform and depending on
the congestion levels in the platform. Door closing confirmation time fluctuates dependingon these characteristics.
After door close, staff confirm safety along cars in the same way as during door closing
confirmation operations. Safety confirmation time also fluctuates depending on these

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Distance

Number of alighting
passengers for eachcar and door

Ticket gateegress data

Congestion level
as of arrival

Number of boarding
Ticket gatepassengers for each
ingress data
car and door
Width
Congestion levelof dooras of departure‫ܣ‬௜ǡ௝ǡ௞ǡ௟

Station i‫ܥ‬௜ǡ௝

Train headway
Door closing time:
2 seconds
Buffer timeܵ௜ǡ௝
Staff operation

Small sudden event

Station i-1ܴ௜ǡ௝‫؞؞؞‬Factor

Time

Figure 1: Factor related chart on train diagram
characteristics.
We estimated each time model in dwell time considering the abovementioned causes.

4 Train Travel Time Simulation Outline
The outline of the simulation is illustrated in Figure 2. In the initial condition, the simulation
starts with 78 trains running on the Tozai line in direction of the city center between 6:30
to 10:00 distributed along stations.
First, departure times and number of passengers for each car at each starting station are
input. Departure time data is acquired from train traffic record data which is obtained fromelectric circuit on a track. at each station. Passenger number data is acquired from a fiveday on-site inspection conducted on November 2015. Then, each time that makes up dwell
time is estimated for each train.
In terms of the number of passengers alighting and boarding, ticket gate ingress andegress count record data aggregated in 30-minute intervals is utilized. Using these data, thenumber of alighting passengers is allocated to each train and car based on passenger
congestion degree. The number of boarding passengers is allocated based on train headways.
Furthermore, the calculated number of passengers is allocated to each door based on rate ofdoor utilization observed during the on-site inspections. We model alighting and boarding
times using linear regression analysis.
Door closing confirmation time and safety confirmation time are estimated based on onsite inspection results. Especially during door closing confirmation time, there are somesmall sudden events such as passengers rushing onto a train, passengers agglomerating inspecific cars and doors, objects getting caught in doors etc. These events must be considered
to build a more detailed simulation. In this study, these events are applied by SmirnoffGrabs test.
Running time is calculated depending on whether the buffer time is negative or positive.
Minimum headways are determined by the signalling system, so excess of planned runningtimes are influenced by the negative buffer time at each station.
4.1 Estimation of alighting and boarding times
To estimate alighting and boarding times, the number of passengers should be calculated.
Ticket gate egress and ingress data is utilized to estimate them. The cumulative distribution

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stations

Leg between

Station i-1
Departure timeٛTrain traffic record dataٜ

Station i
Arrival time

Train jْRunning time modelٓ
Ri,jٛTrain traffic record dataٜ
Number of alighting
passengers for each door

Maximum time
Max(Ai,j,k,l +Bi,j,k,l)

Number of reboarding passengers

ْAlighting time modelٓ
Ai,j,k,lٛOn-site dataٜCar utilization ratio
ٛCar weight dataٜ

Allocation basedon congestion levelon arrival

Door utilizationratioٛ On-site data ٜ

Car utilizing ratio
ٛCar weight dataٜ

Allocation basedon interval between
train arrival times

Station iْClosing confirmation time modelٓ
Ci,jٛOn-site dataٜ

stations
stationsi

Leg between

StationLeg between

Station i+1

Ticket gate ingress dataْ

Number of passengers
each car as of departure

Running time

Dwell time

Dwell time

Number of passengers
each car as of departure

Alighting and boarding
passengers for each car
Departure
time

Number of passengers
each car as of departure

Running time

Running time

Dwell time

Dwell time

Alighting and boarding
passengers for each car
Departure
time

Number of passengers
each car as of departure

Train nTrain j+1
Departuretime

Running time

Departuretime

Station i
Number of passengers
each car as of departure

ٓ: Model, Testٛ ٜ: Data

Train jAlighting and boarding
passengers for each car

Ratio of direction forcity centerٛ On-site data ٜٛTicket gate OD recordْٜSmirnoff-Grubbs testْٓSafety confirmation time modelٓ
Si,jٛOn-site dataٜ
Station i
Departure time
Number of passengers
each car as of departure

Ticket gate egress data

Door utilizationratioٛ On-site data ْٜBoarding time modelٓ
Bi,j,k,lٛOn-site dataٜ
Number of boarding
Dwell time
passengers for each door
Di,jDeparturetime

Station i-1
Number of passengers
each car as of departureٛOn-site dataٜ

Alighting and boarding
passengers for each car
Departure
time

Number of passengers
each car as of departure……

‫؞؞؞‬Calculation procedure

Departure
time

Number of passengers
each car as of departure

Running time

Dwell time……

Alighting and boarding
passengers for each car
Departure
time

Number of passengers
each car as of departure

Running time

Dwell time
Alighting and boarding
passengers for each car
Departure
time

Number of passengers
each car as of departure‫؞؞؞‬Calculation for each train

Figure 2: Simulation flow
is approximated by Gompertz curve (Figure 3), and the number of egress and ingress
passengers in second-scale are derived. In a precise sense, time differences between ticket
gate and train door should be considered. In this simulation, the time difference between
ticket gate and the most time-consuming door to alight and board is considered, and thetime difference is adjusted on the curve.
In terms of the ticket gate egress, the data has OD record for each 30-minute timeinterval, and boarding direction of egress passenger is observed. To distribute egress
passengers to each train, the total number of egress passengers are calculated as following(2) to (4). Furthermore, train direction to the city center is defined as A and train direction
to the suburbs is defined as B.

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30,000

3DVVHQJHU

25,000

Cumulative ingress number
Cumulative egress number

20,00015,00010,000
5,000
0
7:00

8:00

9:00

10:00

7LPH

Figure 3: Example of cumulative distribution of ticket gate ingress and egress number‫݃ܧ‬ௗǡ௜ǡ௧ǡ஺ =‫݃ܧ‬ௗǡ௜ǡ௧ǡ஺Ƭ஻ ή ‫ܴܽܦ‬ௗǡ௜ǡ௧ǡ஺

(2)ܺௗǡ௜ ሺሻ ൌ ܺԢௗǡ௜ ሺ ൅ ‫ݒ݋ܯ‬௜ǡ஺ ሻ(3)ܰ‫ܣ‬ௗǡ௜ ൌ ܺௗǡ௜ ൫‫ݏ‬ௗǡ௜ǡ଻଼ ൯ െ  ܺௗǡ௜ ൫‫ݏ‬ௗǡ௜ǡଵ ൯(4)‫݃ܧ‬ௗǡ௜ǡ௧ : The number of egress passengers on day d at station i on time zone t‫ܴܽܦ‬ௗǡ௜ǡ௧ǡ஺ : Rate of direction A on day d at station i on time zone tܺԢௗǡ௜ (s): Function of cumulative distribution approximated ‫݃ܧ‬ௗǡ௜ǡ௧ǡ஺ܺௗǡ௜ (s): Function of cumulative distribution adjusted the time difference on ܺԢௗǡ௜ (s)‫ݏ‬ௗǡ௜ǡ௝ : Arrival time on day d at station i of train j‫ݒ݋ܯ‬௜ǡ஺ : Time difference between ticket gate and the most time-consuming door to alightand board at station i for direction Aܰ‫ܣ‬ௗǡ௜ : Total number of alighting passengers on day d for direction A
In general, the number of alighting passengers for each train is influenced by those ofpassenger congestion degree on arrival. Given that ܰ‫ܣ‬௜ǡ௧ǡ஺ is the number of alighting
passengers at station i on time zone t for direction A, the equations are expressed as (5) and(6).
ܰ‫ܣ‬ௗǡ௜ǡ௝ ൌ ܰ‫ܣ‬ௗǡ௜ ή ሺ‫݊݋ܥݎݎܣ‬ௗǡ௜ǡ௝ Ȁ‫݊݋ܥݎݎܣ‬ௗǡ௜ǡ௃ )(5)‫݊݋ܥݎݎܣ‬ௗǡ௜ǡ௃ ൌ ෍ ‫݊݋ܥݎݎܣ‬ௗǡ௜ǡ௝ 

(6)

௝‫א‬௃

ܰ‫ܣ‬ௗǡ௜ǡ௝ : The number of alighting passengers on day d at station i on train j‫݊݋ܥݎݎܣ‬ௗǡ௜ǡ௝ : Passenger congestion degree on arrival on day d at station i on train j‫ܬ‬: Set of train j

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 201919

On the other hand, ticket gate ingress data doesn’t have OD record. Thus, the numberof boarding passengers and rate of direction A are calculated using on-site inspection dataand ‫݃ܧ‬ௗǡ௜ǡ௧ǡ஺ . The number of passengers at the time of departure for direction A is calculated
that the number of passengers on arrival plus alighting passengers minus boarding
passengers.
The equations are expressed as (7) and (8). To simulate on the day which is notinspection days, ‫ܾܴܦ‬ௗǡ௜ǡ஺ is adopted as average rate.
‫݃݊ܫ‬ௗǡ௜ǡ௧ǡ஺ ൌ ሺ ෍ ‫݊݋ܥ݌݁ܦ‬ௗǡ௜ǡ௝ െ  ෍ ‫݊݋ܥݎݎܣ‬ௗǡ௜ǡ௝ ሻ ൅ ‫݃ܧ‬ௗǡ௜ǡ௧ǡ஺௝‫א‬௃೔ǡ೟(7)௝‫א‬௃೔ǡ೟‫ܾܴܦ‬ௗǡ௜ǡ஺ ൌ ෍ ‫݃݊ܫ‬ௗǡ௜ǡ௧ǡ஺ Ȁ ෍ ‫݃݊ܫ‬ௗǡ௜ǡ௧ǡ஺Ƭ஻ ௧(8)௝‫א‬௃೔ǡ೟‫݃݊ܫ‬ௗǡ௜ǡ௧ : The number of ingress passengers on day d at station i on time zone t‫݊݋ܥ݌݁ܦ‬ௗǡ௜ǡ௝ : The number of passengers at the time of departure on day d at station i oftrain j for direction A‫ܾܴܦ‬ௗǡ௜ǡ஺ : Average rate of direction A on day d at station i on time zone t
Ji,t: Set of train j at station i on time zone t
Using ‫ܾܴܦ‬௜஺ and ticket gate ingress data, the number of boarding passengers each train
is calculated as following (9) to (11). Since it is difficult to grasp how long it takes forpassengers to get on the train during dwell time, then the number of boarding passengers
each train is defined as the cumulative numbers between subsequent train’s arrival time andfollowing train’s arrival time.
‫݃݊ܫ‬ௗǡ௜ǡ௧ǡ஺ ൌ ‫݃݊ܫ‬ௗǡ௜ǡ௧ǡ஺Ƭ஻ ή ‫ܾܴܦ‬௜஺

(9)ܻௗǡ௜ ሺሻ ൌ ܻԢௗǡ௜ ሺ െ ‫ݒ݋ܯ‬௜ǡ஺ ሻ(10)ܰ‫ܤ‬ௗǡ௜ǡ௝ ൌ ܻௗǡ௜ǡ௝ ൫‫ݏ‬ௗǡ௜ǡ௝ ൯ െ  ܻௗǡ௜ǡ௝ ൫‫ݏ‬ௗǡ௜ǡ௝ିଵ ൯(11)‫ܾܴܦ‬௜ǡ஺ : Rate of direction A on day d at station i on time zone tܻԢௗǡ௜ (s): Function of cumulative distribution approximated ‫݃݊ܫ‬ௗǡ௜ǡ௧ǡ஺ܻௗǡ௜ (s): Function of cumulative distribution adjusted the time difference on ܻԢௗǡ௜ (s)ܰ‫ܤ‬ௗǡ௜ǡ௝ : The number of boarding passengers on day d at station i on train j for direction A
To distribute alighting and boarding passengers to each car and door, utilization rate ofcars and doors must be estimated. Utilization rate of car each station is estimated from carweight data acquired between October 2015 and December 2015. And utilization rate ofeach door is grasped from the on-site inspection results. Both rates are implemented as fixed
average value on the simulator.
4.2 Alighting time model
In terms of alighting time, two significant parameters are adopted, one is the number ofalighting passengers and second is wider doors described earlier. To create the model, we

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 201920

M easured alighting time (Sec)Estimated alighting time (Sec)Figure 4: Relationship between measured value and estimated value in alighting time
utilize the video recording data which records passenger alighting and boarding on theplatform at each station. Alighting number of passengers counting data which is each 391
samples is given by video data. Passenger congestion degree and wider door are determinedfrom on-site inspections. In fact, passenger flow on platform affects dwell time. However,
it is assumed that the model expresses the effects due to on-site inspection results including
the flow.
In the alighting time regression model, explanatory variables are the number of alighting
passengers and the presence or absence of wider door. The equation is expressed as (12).
Figure 4 and Table 1 show the results.
(12)‫ܣ‬௜ǡ௝ǡ௞ǡ௟ ൌ ߙ ൅ ߚଵ ή ܰ‫ܣ‬௜ǡ௝ǡ௞ǡ௟ ൅ ߚଶ ή ܹ݅݀݁ ൅ ᖡAi, j, k, l: Alighting time at station i of train j, car No. k, door No. lNAi, j, k, l: The number of alighting passengers at station i of train j, car No. k, door No. l
Wide: Wider door dummy
DE1, E2: Parameter
HError term
Parameter
Intercept
Number of alighting
passengers
Wider door dummy

Table 1: Result of alighting time model
Coefficientt value
4.89
19.97
0.52
R2: 0.83

p value
1.56E-61

43.58

1.5E-151

-1.52
-6.19
Sample: 391 trains

1.5E-09

The result obtains good fit by R20.83, however there is variability between measured
value and estimated value due to uncertain passenger flow. Therefore, the estimated valueof alighting time is given by adding the normal random value of estimation error..

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 201921

M easured boarding time (Sec)Estimated boarding time (Sec)Figure 5: Relationship between measured value and estimated value in boarding time
4.3 Boarding time model
In terms of boarding time, two parameters are estimated, the number of boarding passengers
and passenger congestion degree in the car as of departure. To create the model, we utilize
the video recording data as is the case with alighting model. Boarding number of passengers
counting data has also 391 samples.
In the boarding time model, since boarding time tends to extend due to congestion, andthis distribution increase towards one side the dependent variable is log-transformed.
Explanatory variables are the number of boarding passengers and passenger congestion
degree in the car as of departure. The equation is expressed as (13).
݈‫ܤ݃݋‬௜ǡ௝ǡ௞ǡ௟ ൌ ߙ ൅ ߚଵ ή ܰ‫ܤ‬௜ǡ௝ǡ௞ǡ௟ ൅ ߚଶ ή ‫݊݋ܥ݌݁ܦ‬௜ǡ௝ǡ௞ ൅ ᖡ(13)Bi, j, k, l: Boarding time at station i of train j, car No. k, door No. l
NBi, j, k, l: The number of boarding passengers at station i of train j, car No. k, door No. l
DepConi, j, k: Passenger congestion degree at departure time at station i of train j, car No. k
DE1, E2: Parameter
HError term
Figure 5 and Table 2 show the estimation results. The result obtains good fit from R20.67,
however there is variability between measured value and estimated value due to uncertainly
passenger flow. Therefore, the estimated value of boarding time is given by adding thenormal random value of estimation error.
Table 2: Result of boarding time modelParameter
Coefficientt value
Intercept
0.63
17.56
Number of boarding passengers
0.030
26.96
Passenger congestion degree at departure
0.00051
2.36
R2: 0.67
Sample: 391 trains

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 2019

p value
3.37E-51
6.1E-910.019

22Distanceܵ‫݊݋݅ݐܽݐ‬௜ାଵ‫ܪ‬௜ǡ௝

‫ܦ‬௜ǡ௝

‫ܪܯ‬௜‫ݑܤ‬௜ǡ௝ܵ‫݊݋݅ݐܽݐ‬௜‫ܦ‬ǯ௜ǡ௝‫ܪܯ‬௜ܵ‫݊݋݅ݐܽݐ‬௜ିଵ‫ܪ‬௜ିଵǡ௝ାଵܴ௜ିଵǡ௝
ܴԢ௜ିଵǡ௝‫ܪ‬Ԣ௜ିଵǡ௝ାଵܴ௜ିଵǡ௝ାଵ

TimeܴԢ௜ିଵǡ௝ାଵ

Figure 6: Mechanism of train delay propagation
4.4 Estimation of door closing confirmation time
Door closing confirmation time depends on station staff operations. To estimate door
closing confirmation time, normal random numbers were simulated based on the
distribution observed during the on-site inspections at each station. Moreover, detailed
analysis of the time should consider small sudden events that happen frequently. The eventsare considered that a dwell time excess degree is discerned by Smirnoff-Grabs test basedon long term dwell time records.
Regarding train j, the test statistics is defined as ݆ܶ , the logarithmic value of dwell timeഥ݆ , the standard
is defined as ݆ܺ , the average of logarithmic value of dwell time is defined as ܺ
deviation is defined as ‫ ݆ݏ‬, the equation is expressed as (14). This judgement is focused on
excess dwell time, so one sided-testing is adopted.㻌ܶ௝ ൌ ሺܺ௝ െ ܺഥఫ ሻȀ‫ݏ‬௝

(14)

4.5 Estimation of safety confirmation time
Safety confirmation time also depends on station staff operations. As such, similar to door
closing confirmation time. normal random numbers were simulated based on the
distribution observed during the on-site inspections at each station.
4.6 Estimation of running time
To estimate running time, buffer time is considered. If the buffer is positive, the train would
run following the planned running time. However, if the buffer time is negative, subsequent
trains slow down or stop between stations because they are too close to the preceding train.
The buffer time is determined by the signalling system design at each station. The
phenomenon is illustrated in Figure 6, 7 and following (15) and (16). In figure 6, train
headway (H) is segmented into 3 parts: dwell time (D), minimum headway which isdetermined by signaling system each station (MH), buffer time (Bu), running time (R). The
red lines are expressed actual train behavior, and red letters with dash are actual time.

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 201923

Running time (min, s ec)

6:00
5:00
4:00
3:00
2:00
1:00

Buffer timeʽ̎

Buffer ti meӍ̎

0:00
-3:00

-2:00

-1:00

0:00

1:00

2:00

3:00

Buffer ti me(min, sec)

Figure 7: Relationship between Buffer time and Running time‫ݑܤ‬Ԣ௜ǡ௝ାଵ =‫ܪ‬Ԣ௜ିଵǡ௝ାଵ -(ܴԢ௜ିଵǡ௝ -ܴ௜ିଵǡ௝ )-‫ܦ‬Ԣ௜ǡ௝ -‫ܪܯ‬௜ǡ௝ାଵ ൏0(15)‫ݑܤ‬Ԣ௜ǡ௝ାଵ =‫ܪ‬Ԣ௜ିଵǡ௝ାଵ -(ܴԢ௜ିଵǡ௝ -ܴ௜ିଵǡ௝ )-‫ܦ‬Ԣ௜ǡ௝ -‫ܪܯ‬௜ǡ௝ାଵ ൒0(16)

In terms of the relationship between buffer time and running time, with increasing
negative buffer time, running time increases linearly (See Figure 7). Utilizing this linearity
property, running time between stations is calculated.. When buffer time is positive, thetrain driver can adjust to recover lost time, but train driver operation is different with eachdriver, therefore, in the simulation, when buffer time is positive, trains run according to theplanned running time.
4.7 Adjustment of train headway
In daily operations, if there is change in train headways, the control center operator adjusts
the headways to prevent agglomerate of passenger congestion. If the train interval is longer
than 1 minute 30 seconds and less than 2 minutes compared to the planned headway at thetime of the departure, the preceding train is adjusted by a planned dwell time + 1 minuteafter the departure time. In the same way, the train interval is longer than 2 minutes and lessthan 2 minutes and 30 seconds, the adjustment time of preceding train is planned dwell time
+ 1 minute and 30 seconds.
In usual situations, the number of boarding passengers is calculated between arrival
times. However, in the case of headway adjustment, the number of boarding passengers is
calculated between arrival time of subsequent train and the time which subtract departuretime of following train considered adjustment from the door closing confirmation time andthe safety confirmation time.

5 Assessment of simulation reproducibility
To confirm that the simulation reproducibility and its accuracy is maintained, we put into
the departure time and congestion data at starting station which is the 5 days data based on
the construction of the simulation, then simulate 100 times for each day. Residual error
RMS (Root mean square) is adopted as the performance index.

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 201924

Travel time (min:sec)

Measured average travel time(5 days of 2015.11) : left axis
Simulated average travel time(Random days) : left axis
Measured average travel time(Random days) : left axis
Residual error RMS(5 days of 2015.11) : right axis
Residual error RMS(Random days) : right axis

Residual error RMS (min:sec)

Simulated average travel time(5days of 2015.11) : left axis

Train

Figure 8: Simulation results of reproducibility
Further, we simulated 100 times for 10 days at random excluding the 5 days. There’s no
way to get some data on random days, we estimate them as follows.
Congestion degree of those random days at starting station is figured out based on
proportion of the 5 days average degree to those of degree on random days.
The number of alighting passengers each station in random days is figure out based onequation (2) to (4). Ticket gate ingress and egress data replace the 5 days data with random
days data, and rate of direction is adopted average rate of direction A on the 5 days. The
number of boarding passengers each station in random days is figure out based on equation(9) to (11). Ticket gate ingress and egress data replace the 5 days data with random days
data, and rate of direction is adopted average rate of direction A on the 5 days. In addition,
wider door is set at random.
Figure 8 shows the results of the reproducibility test. The actual average of travel time
is 17 minutes and 13 seconds and standard deviation is 1 minute and 22 seconds, andsimulated that time is 17 minutes and 16 seconds and standard deviation is 1 minute and 31
seconds. High accuracy is maintained compared to references. Also, in the case of the data
selected at random, those of simulated travel time is confirmed high accuracy that error
between travel time and standard deviation are few seconds.
5.1 Case study for improvement of train delay
Railway companies have taken measures to improve train delay and train congestion. There
are two types of measures, one is improvement of train timetable, second is distribution ofpassenger congestion. The former measure aims at avoiding delay propagation tosubsequent trains. Important point to avoid propagation is to expand buffer times. This is

8th International Conference on Railway Operations Modelling and Analysis - RailNorrköping 201925

30,000

Passenger

25,000

Cumulative ingress number
Cumulative egress number
Allocation based oncongestion degree

20,00015,00010% or 20%

10,00010% or 20%

5,000
0
7:00

8:00

9:00

10:00

Time

Figure 9: Allocation of passenger demandalso conducted by daily operation at control center.
The latter measure aims at distributing congestion agglomeration of specific cars anddoors. Station staff encourage passengers to use more empty cars or use earlier trains. In
2017 summer, Tokyo metropolitan government implemented “Jisa Biz” staggered
commuting campaign and many companies addressed changes in work start time during the
campaign term. In 2020, the Tokyo Olympic and Paralympic Games will be held. Especially,
congestion of peak-hour adding spectators would over the limit of train transportation
capacity in Tokyo. The government would like to build staggered commuting as routine by
2020. Furthermore, for legacy, staggered commuting would be conductive to smoothtransports and flexible lifestyles.
Utilizing the proposed simulation, we estimate the effect of staggered commuting on
Tozai line focusing on one day. Passengers demand on starting station during 7:30 to 8:29
reduce 10%, and the 10% passengers are allocated to each train running on time zone 6:30
to 7:29 based on each train congestion degree. And boarding passengers during 8:00 to 8:29
and 8:30 to 8:59 reduce 10%, and the 10% passengers are allocated to each train runningon time zone 7:00 to 7:29 and 7:30 to 7:59 based on each train passenger congestion degree.
The number of alighting passengers is calculated as same way of boarding case.
Furthermore, in the case of 20% reduce is calculated as same way (Figure 9).
Figure 10 shows the results. The actual average travel time is 16 minutes and 20 seconds,
and passenger 10% moving case is 16 minutes and 14 seconds and that of 20% moving caseis 16 minutes and 11 seconds. The average travel time is alleviated due to demand moving.
Particularly, before peak hour, travel time increases by 17 seconds in the case of 10%
moving case, and 28 seconds in the case of 20% moving case. On the other hand, on peakhour, the maximum improvement time is 24 seconds in the case of 10% moving case, and41 seconds in the case of 20% moving case. The effects have decent improvement, but
further demand moving deal is necessary for legacy.

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Travel time (min:sec)

Difference value (min:sec)

Time improvement (10% reduce): left axis
Time improvement (20% reduce): left axis
Change 20 % demand: right axis

Change 10 % demand: right axis
Actual demand: right axis

Train

Figure 10: Simulation results of demand change

From this result, it is confirmed that travel time before peak hour increase temporarily,
but travel time at peak hour improve well and average travel time is also shortened.

6 Conclusion
We have introduced an innovative method of train travel time simulation model utilizing
daily ticket gate ingress and egress data and detailed on-site inspection results. Especially,
focussing on each time model in dwell time is new characteristic of the simulation. Also,
utilizing past traffic record data to model sudden small events during closing confirmation
time is reproduced detailed situation. We obtained high reproducibility and confirm theusefulness of the proposed method. In the case of the staggered commuting campaign, we
confirmed the effect of travel time change due to moving passenger demand. In this case,we confirmed certain level of peak hour improvement. However, for flexible commuting,staggered activities should be promoted more.
In order to contribute to the improvement of passenger congestion and train delays,
further work should consider the characteristics of different lines and different situation ofpassenger alighting and boarding situations and simulate more cases reflecting otherdemand change deal. station situation and actual operations more.

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