Please wait a minute...
Shopping Cart Email List Chinese
Advanced Search Fig/Tab
Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Featured Articles  |  Most Downloaded  |  Most Reading
Online Submission Subscribe to Our RSS Content Feed
Front. Eng    2017, Vol. 4 Issue (4) : 388-398    https://doi.org/10.15302/J-FEM-2017046
RESEARCH ARTICLE
Understanding network travel time reliability with on-demand ride service data
Xiqun (Michael) CHEN1(), Xiaowei CHEN2, Hongyu ZHENG2, Chuqiao CHEN2
1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
2. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
 Download: PDF(1523 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Travel time reliability is of increasing importance for travelers, shippers, and transportation managers because traffic congestion has become worse in major urban areas in recent years. To better evaluate the urban network-wide travel time reliability, five indices based on the emerging on-demand ride service data are proposed: network free flow time rate (NFFTR), network travel time rate (NTTR), network planning time rate (NPTR), network buffer time rate (NBTR), and network buffer time rate index (NBTRI). These indices take into account the probability distribution of the travel time rate (i.e., travel time spent for the unit distance, in min/km) of each origin-destination (OD) pair in the road network. We use real-world data extracted from DiDi-Chuxing, which is the largest on-demand ride service platform in China. For demonstrative purposes, the network-wide travel time reliability of Beijing is analyzed in detail from two dimensions of time and space. The results show that the road network is more unreliable in AM/PM peaks than other time periods, and the most reliable time period is the early morning. Additionally, we can find that the central region is more unreliable than other regions of the city based on the spatial analysis results. The proposed network travel time reliability indices provide insights for the comprehensive evaluation of the road network traffic dynamics and day-to-day travel time variations.
Keywords network travel time reliability      on-demand ride services      travel time rate      OD     
Corresponding Author(s): Xiqun (Michael) CHEN   
Just Accepted Date: 13 September 2017   Online First Date: 31 October 2017    Issue Date: 14 December 2017
 Cite this article:   
Xiqun (Michael) CHEN,Xiaowei CHEN,Hongyu ZHENG, et al. Understanding network travel time reliability with on-demand ride service data[J]. Front. Eng, 2017, 4(4): 388-398.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2017046
https://academic.hep.com.cn/fem/EN/Y2017/V4/I4/388
Fig.1  An illustration of the travel time rate distribution for each OD pair
  Spatial distribution of on-demand ride request origins in Beijing for the year 2016
NFFTR/(min·km–1) NTTR/(min·km–1) NPTR/(min·km–1) NBTR/(min·km–1) NBTRI
1.52 2.38 4.17 1.78 75.67%
Tab.1  Yearly travel time reliability indices for Beijing in 2016
Fig.2  Monthly distribution of travel time reliability indices in Beijing
Fig.3  Daily distribution of travel time reliability indices in Beijing
Fig.4  Hourly distribution of travel time reliability indices in Beijing
FFTR /(min·km–1) TTR/(min·km–1) PTR/(min·km–1) BTR/(min·km–1) BTRI
1.91 2.94 5.00 2.07 71.03%
Tab.2  Origin-based travel time reliability indices of Tiananmen in Beijing (2016)
Month NFFTR/(min·km–1) NTTR/(min·km–1) NPTR/(min·km–1) NBTR/(min·km–1) NBTRI Sampling outflow/ (trips·year–1)
January 2.26 3.67 5.70 2.03 56.03% 549
February 2.09 2.47 4.10 1.64 66.37% 274
March 2.97 3.65 5.00 1.36 36.91% 551
April 2.30 3.59 5.55 1.97 55.61% 806
May 2.57 3.76 5.75 1.99 51.94% 939
June 2.58 3.71 5.96 2.25 61.50% 911
July 2.63 3.88 5.83 1.96 53.07% 920
August 2.48 4.01 6.26 2.25 56.80% 817
September 2.41 3.42 5.00 1.59 47.00% 733
October 2.40 3.48 5.78 2.30 68.97% 669
November 2.32 3.22 4.97 1.75 56.49% 515
December 2.07 3.01 4.58 1.57 53.35% 545
Tab.3  Monthly origin-based travel time reliability indices of Tiananmen in Beijing (2016)
Day of week FFTR/(min·km–1) TTR/(min·km–1) PTR/(min·km–1) BTR/(min·km–1) BTRI Sampling outflow /(trips·year–1)
Monday 2.14 3.14 4.92 1.78 56.07% 1243
Tuesday 2.26 3.26 5.23 1.97 62.34% 1128
Wednesday 2.29 3.41 5.27 1.85 55.09% 1132
Thursday 2.30 3.44 5.46 2.02 60.27% 1153
Friday 2.37 3.37 5.11 1.75 53.29% 1123
Saturday 2.29 3.50 5.57 2.07 58.50% 1132
Sunday 2.28 3.46 5.80 2.34 69.07% 1318
Tab.4  Daily origin-based travel time reliability indices of Tiananmen in Beijing (2016)
Fig.5  Spatial distribution of the origin-based buffer time rate index in Beijing (red for low reliability, orange for median reliability, and green for high reliability)
Hour FFTR/(min·km–1) TTR/(min·km–1) PTR/(min·km–1) BTR/(min·km–1) BTRI Sampling outflow /(trips·year–1)
0 h 2.20 3.18 4.80 1.61 51.76% 348
1 h 2.23 3.10 4.85 1.75 56.85% 207
2 h 2.34 3.22 4.88 1.66 52.69% 116
3 h 2.28 3.15 4.81 1.66 52.79% 72
4 h 2.15 2.96 4.34 1.38 47.54% 40
5 h 2.32 3.20 4.86 1.66 53.14% 41
6 h 2.41 3.26 5.05 1.79 55.88% 79
7 h 2.44 3.30 5.07 1.77 54.40% 228
8 h 2.44 3.42 5.15 1.73 51.19% 349
9 h 2.60 3.73 5.59 1.85 50.93% 445
10 h 2.39 3.37 5.14 1.77 53.30% 425
11 h 2.21 2.97 4.29 1.32 45.36% 434
12 h 2.15 2.87 4.14 1.27 45.18% 436
13 h 2.07 2.74 3.81 1.07 39.06% 441
14 h 1.90 2.48 3.54 1.06 43.77% 472
15 h 1.66 2.12 2.92 0.80 37.70% 444
16 h 1.85 2.27 3.14 0.87 36.56% 430
17 h 1.94 2.28 3.71 1.43 59.11% 451
18 h 1.83 2.17 2.88 0.72 33.52% 406
19 h 1.81 2.27 3.60 1.33 46.12% 393
20 h 1.91 2.58 3.36 0.78 30.38% 420
21 h 1.91 2.29 3.17 0.87 35.93% 527
22 h 1.95 2.53 3.77 1.23 46.62% 535
23 h 2.32 3.40 5.27 1.87 56.30% 490
Tab.5  Hourly origin-based travel time reliability indices of Tiananmen in Beijing (2016)
Location FFTR/(min·km–1) TTR/(min·km–1) PTR/(min·km–1) BTR/(min·km–1) BTRI Sampling outflow/(trips·year–1)
Capital International Airport 1.20 1.67 2.74 1.07 65.16% 1781
Beijing Railway Station 1.78 2.83 4.95 2.11 75.49% 21919
Beijing South Railway Station 1.54 2.33 3.96 1.63 71.22% 10829
Beijing North Railway Station 1.67 2.58 4.37 1.79 70.84% 14791
Beijing West Railway Station 1.48 2.32 4.08 1.76 76.62% 13479
Beijing East Railway Station 1.70 2.82 5.01 2.19 78.88% 27188
Tab.6  Origin-based travel time reliability indices of transportation hubs
Location FFTR/(min·km–1) TTR/(min·km–1) PTR/(min·km–1) BTR/(min·km–1) BTRI Sampling outflow/(trips·year–1)
Tiananmen 1.91 2.94 5.00 2.07 71.03% 8229
Temple of Heaven 1.87 2.81 4.65 1.84 66.58% 7113
The Summer Palace 1.76 2.50 4.34 1.84 73.75% 1185
798 Art Zone 1.78 2.75 4.70 1.95 71.43% 14901
Sanlitun Bar Street 1.73 2.84 5.08 2.24 79.91% 27658
Tab.7  Origin-based travel time reliability indices of tourist attractions
Location FFTR/(min·km–1) TTR/(min·km–1) PTR/(min·km–1) BTR/(min·km–1) BTRI Sampling outflow/(trips·year–1)
Wangfujing 1.93 3.01 5.13 2.12 70.23% 12429
Dashilar 1.95 2.95 4.90 1.95 65.81% 9093
China World Mall 1.78 2.97 5.28 2.31 79.45% 27503
Oriental Plaza 1.81 2.85 4.93 2.08 73.33% 17696
Xidan 1.87 2.93 4.94 2.02 69.19% 17994
Tab.8  Origin-based travel time reliability indices of business centers
Parameter notation.
i The serial number of the origin grid
j The serial number of the destination grid
k the kth trip of a certain OD pair
d ijk Travel distance of trip k from origin i to destination j
τ Random variable of travel time rate
τ ijk Travel time rate of the trip k from origin i to destination j
τ ‾ i j Mean travel time rate of the OD pair (i, j)
τ ij,α Travel time rate percentile at the confidence level of α for the OD pair (i, j)
f ij,(τ) Probability density function of τ for the OD pair (i, j)
F ij,(τ) Cumulative distribution function of τ for the OD pair (i, j)
n ij Number of trips from origin i to destination j
t ijk Travel time of trip k from origin i to destination j
w ij Weight of the OD pair (i, j)
β ij Buffer time rate of the OD pair (i, j)
η ij Buffer time rate index of the OD pair (i, j)
NBTR Network buffer time rate
NBTRI Network buffer time rate index
NFFTR Network free-flow travel time rate
NPTR Network planning time rate
NTTR Network travel time rate
  
1 A K Abir (2016). The value of travel time and reliability-empirical evidence from katy freeway. Dissertation for the Doctoral Degree. Texas: Texas A&M University
2 Y Asakura, E Hato, M Kashiwadani (2003). Stochastic network design problem: an optimal link investment model for reliable network. In: Proceedings of the 1st International Symposium on Transportation Network Reliability, 245–260. Bingley: Emerald Group Publishing Limited
3 Y Asakura, M Kashiwadani (1991). Road network reliability caused by daily fluctuation of traffic flow. In: The 19th PTRC Summer Annual Meeting, University of Sussex, United Kingdom
4 M G H Bell, C Cassir (2002). Risk-averse user equilibrium traffic assignment: an application of game theory. Transportation Research Part B: Methodological, 36(8): 671–681
https://doi.org/10.1016/S0191-2615(01)00022-4
5 N Bhouri, J Kauppila (2011). Managing highways for better reliability: assessing reliability benefits of ramp metering. Transportation Research Record: Journal of the Transportation Research Board, 2229: 1–7
https://doi.org/10.3141/2229-01
6 C Carrion, D Levinson (2012). Value of travel time reliability: a review of current evidence. Transportation Research Part A, Policy and Practice, 46(4): 720–741
https://doi.org/10.1016/j.tra.2012.01.003
7 A Chen, H Yang, H K Lo, W H Tang (2002). Capacity reliability of a road network: an assessment methodology and numerical results. Transportation Research Part B: Methodological, 36(3): 225–252
https://doi.org/10.1016/S0191-2615(00)00048-5
8 X Chen, M Zahiri, S Zhang (2017). Understanding ridesplitting behavior of on-demand ride services: an ensemble learning approach. Transportation Research Part C, Emerging Technologies, 76: 51–70
https://doi.org/10.1016/j.trc.2016.12.018
9 S Clark, D Watling (2005). Modelling network travel time reliability under stochastic demand. Transportation Research Part B: Methodological, 39(2): 119–140
https://doi.org/10.1016/j.trb.2003.10.006
10 P C Devarasetty, M Burris, W Douglass Shaw (2012). The value of travel time and reliability-evidence from a stated preference survey and actual usage. Transportation Research Part A, Policy and Practice, 46(8): 1227–1240
https://doi.org/10.1016/j.tra.2012.05.002
11 Federal Highway Administration (2006). Travel time reliability: making it there on time, all the time.
12 H Gan, Y Bai (2014). The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis. Transportation, 41(2): 339–350
https://doi.org/10.1007/s11116-013-9481-6
13 Jr D P Gaver (1968). Headstart strategies for combating congestion. Transportation Science, 2(2): 172–181
https://doi.org/10.1287/trsc.2.2.172
14 R Herman, T Lam (1974). Trip time characteristics of journeys to and from work. Transportation and Traffic Theory, 6: 57–86
15 L W Hou, F Jiang (2002). Simulation of urban road network reliability . Journal of System Simulation, 14(5): 664–668 (in Chinese)
16 T E Knight (1974). An approach to the evaluation of changes in travel unreliability: A “Safety margin” hypothesis. Transportation, 3(4): 393–408
https://doi.org/10.1007/BF00167968
17 T C Lam, K A Small (2001). The value of time and reliability: measurement from a value pricing experiment. Transportation Research Part E, Logistics and Transportation Review, 37(2–3): 231–251
https://doi.org/10.1016/S1366-5545(00)00016-8
18 T Lomax, D Schrank, S Turner, R Margiotta(2003). Selecting travel reliability measures. Texas Transportation Institute Monograph
19 K Lyman, R Bertini (2008). Using travel time reliability measures to improve regional transportation planning and operations. Transportation Research Record: Journal of the Transportation Research Board, 2046(1): 1–10
https://doi.org/10.3141/2046-01
20 H Mahmassani, T Hou, M Saberi (2013). Connecting networkwide travel time reliability and the network fundamental diagram of traffic flow. Transportation Research Record: Journal of the Transportation Research Board, 2391: 80–91
https://doi.org/10.3141/2391-08
21 R B Noland, J W Polak (2002). Travel time variability: a review of theoretical and empirical issues. Transport Reviews, 22(1): 39–54
https://doi.org/10.1080/01441640010022456
22 H A Rakha, I El-Shawarby, M Arafeh, F Dion (2006). Estimating path travel-time reliability. In: IEEE Intelligent Transportation Systems Conference, Toronto, Ont., Canada, 236–241
23 A J Richardson, M A P Taylor (1978). Travel time variability on commuter journeys. High Speed Ground Transportation Journal, 12(1): 77–99
24 S Sen, R Pillai, S Joshi, A K Rathi (2001). A mean-variance model for route guidance in advanced traveler information systems. Transportation Science, 35(1): 37–49
https://doi.org/10.1287/trsc.35.1.37.10141
25 K A Small, C Winston, J Yan (2005). Uncovering the distribution of motorists’ preferences for travel time and reliability. Econometrica, 73(4): 1367–1382
https://doi.org/10.1111/j.1468-0262.2005.00619.x
26 M A P Taylor(1982). Travel time variability—the case of two public modes. Transportation Science, 16(4): 507–21
27 N Uno, F Kurauchi, H Tamura, Y Iida (2009). Using bus probe data for analysis of travel time variability. Journal of Intelligent Transportation Systems, 13(1): 2–15
[1] Kaile ZHOU, Zenghui ZHANG, Lu LIU, Shanlin YANG. Energy storage resources management: Planning, operation, and business model[J]. Front. Eng, 2022, 9(3): 373-391.
[2] Peng ZHOU, Shuaizhi GAO, Yue LV, Ge ZHAO. Energy transition management towards a low-carbon world[J]. Front. Eng, 2022, 9(3): 499-503.
[3] Xingyu DAI, Ling XIAO, Matthew C. LI, Qunwei WANG. Toward energy finance market transition: Does China’s oil futures shake up global spots market?[J]. Front. Eng, 2022, 9(3): 409-424.
[4] Nan LIU, Jidong KANG, Tsan Sheng NG, Bin SU. Cutting CO2 emissions through demand side regulation: Implications from multi-regional input–output linear programming model[J]. Front. Eng, 2022, 9(3): 452-461.
[5] Yuwen ZHENG, Yifang ZHENG, Guannan HE, Jie SONG. Decomposition and decoupling analysis of electricity consumption carbon emissions in China[J]. Front. Eng, 2022, 9(3): 486-498.
[6] Zhong-Zhong JIANG, Guangqi FENG, Zelong YI, Xiaolong GUO. Service-oriented manufacturing: A literature review and future research directions[J]. Front. Eng, 2022, 9(1): 71-88.
[7] Gongyu WU, Zhaojun S. LI. Cyber–Physical Power System (CPPS): A review on measures and optimization methods of system resilience[J]. Front. Eng, 2021, 8(4): 503-518.
[8] Zili WANG. Current status and prospects of reliability systems engineering in China[J]. Front. Eng, 2021, 8(4): 492-502.
[9] Yu CHENG, Jiateng YIN, Lixing YANG. Robust energy-efficient train speed profile optimization in a scenario-based position–time–speed network[J]. Front. Eng, 2021, 8(4): 595-614.
[10] Jiwei ZHU, Hua GAO, Jiangrui WANG. Analysis of synergy degree and its influencing factors in hydropower EPC project management[J]. Front. Eng, 2021, 8(3): 402-411.
[11] Karina B. BARTH, Carlos T. FORMOSO. Requirements in performance measurement systems of construction projects from the lean production perspective[J]. Front. Eng, 2021, 8(3): 442-455.
[12] Xue YAN, Longmiao GAO, Huimin LIU. Adaptability evolution of financing systems of mega projects[J]. Front. Eng, 2021, 8(2): 258-270.
[13] Conrad BOTON, Yaya PITTI, Daniel FORGUES, Ivanka IORDANOVA. Investigating the challenges related to combining BIM and Last Planner System on construction sites[J]. Front. Eng, 2021, 8(2): 172-182.
[14] Sander SIEBELINK, Hans VOORDIJK, Maaike ENDEDIJK, Arjen ADRIAANSE. Understanding barriers to BIM implementation: Their impact across organizational levels in relation to BIM maturity[J]. Front. Eng, 2021, 8(2): 236-257.
[15] Jie WU, Jiasen SUN, Liang LIANG. Methods and applications of DEA cross-efficiency: Review and future perspectives[J]. Front. Eng, 2021, 8(2): 199-211.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed