Article
Perceived Trip Time Reliability and Its Cost in a Rail
Transit Network
Jie Liu
1,2
, Paul Schonfeld
3
, Jinqu Chen
1
, Yong Yin
1,
* and Qiyuan Peng
1
Citation: Liu, J.; Schonfeld, P.; Chen,
J.; Yin, Y.; Peng, Q. Perceived Trip
Time Reliability and Its Cost in a Rail
Transit Network. Sustainability 2021,
13, 7504. https://doi.org/10.3390/
su13137504
Academic Editors: João Carlos de
Oliveira Matias and Paolo Renna
Received: 15 May 2021
Accepted: 22 June 2021
Published: 5 July 2021
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1
School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China;
JieLiu.SWJTU@outlook.com (J.L.); Chenjinqu@my.swjtu.edu.cn (J.C.); qiyuan-peng@swjtu.edu.cn (Q.P.)
2
Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650093, China
3
A. James Clark School of Engineering, University of Maryland, College Park, MD 20740, USA;
pschon@umd.edu
* Correspondence: yinyong@home.swjtu.edu.cn
Abstract:
Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-
based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected
by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times
are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average
PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the
average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost
(PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer
time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of
obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied
to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows
that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR,
PTRC, and influential factors have significant linear relations among them, and the obtained linear
regression models among them can guide passengers to travel reliably.
Keywords: perceived time; reliability; travel condition; Monte Carlo simulation; linear regression
1. Introduction
As travel demand increases, passengers in large cities experience increasing conges-
tion, crowding, and low time reliability. Time reliability not only reflects the service quality
of transportation networks, but also affects passengers’ route choices [
1
] and travel satisfac-
tion [
2
]. Passengers who require high time reliability, such as commuters, are willing to
pay for such reliability [
3
]. Both transportation managers and passengers consider time
reliability to be very important.
Many studies have explored time reliability for road networks [
4
,
5
], Rail Transit
Networks (RTNs) [
6
,
7
], and bus networks [
8
,
9
]. Although these studies have proposed
measures for evaluating the time reliability of transportation networks, travel conditions
such as the number of transfer times, crowding, and seat availability in vehicles that affect
passengers’ perceptions have been relatively neglected [
10
]. The reason is that time reliabil-
ity is measured based on clock-based trip time, which neglects travel conditions. Let us
assume that passengers A and B travel between the same Origin to Destination Station (OD)
pair and spend the same time to arrive at the destination station. However, if passenger A
gets a seat in an uncrowded vehicle while passenger B stands in a crowded vehicle, their
travel conditions vary greatly despite having the same time reliability. Therefore, both
clock-based trip time and the passengers’ travel conditions are incorporated here to mea-
sure the Perceived Time Reliability (PTR) and Perceived Time Reliability Cost (PTRC). The
extra PTT needed for arriving reliably equaling the difference between the 95th percentile
PTT and average PTT, is converted into the monetary cost for estimating PTRC. Referring
Sustainability 2021, 13, 7504. https://doi.org/10.3390/su13137504 https://www.mdpi.com/journal/sustainability
