Transit accessibility measures incorporating the temporal dimension

Xu, Wangtu; Yanjie, Ding; Zhou, Jiangping; Li, Yuan

doi:10.1016/j.cities.2015.05.002

Cited by 68 publications

(23 citation statements)

References 40 publications

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“…The analytical results are as follows: the distribution of TCI in the AM peak hours and PM peak hours are shown in Figures 7(a)-7(b). We can see a totally different picture of TCI in the AM peak hours as compared to that in PM peak hours, which indicates that some of the TCI vary during different periods while the transit routes and departure interval are the same, which is similar to the findings of [37]. Based on those pictures, we should have different principles and strategies in terms of deploying our bus-related resources and services.…”

Section: 2mentioning

confidence: 72%

A Novel Trip Coverage Index for Transit Accessibility Assessment Using Mobile Phone Data

Cai

Wang

Chen

2017

Journal of Advanced Transportation

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Transit accessibility is an important measure on the service performance of transit systems. To assess whether the public transit service is well accessible for trips of specific origins, destinations, and origin-destination (OD) pairs, a novel measure, the Trip Coverage Index (TCI), is proposed in this paper. TCI considers both the transit trip coverage and spatial distribution of individual travel demands. Massive trips between cellular base stations are estimated by using over four-million mobile phone users. An easy-to-implement method is also developed to extract the transit information and driving routes for millions of requests. Then the trip coverage of each OD pair is calculated. For demonstrative purposes, TCI is applied to the transit network of Hangzhou, China. The results show that TCI represents the better transit trip coverage and provides a more powerful assessment tool of transit quality of service. Since the calculation is based on trips of all modes, but not only the transit trips, TCI offers an overall accessibility for the transit system performance. It enables decision makers to assess transit accessibility in a finer-grained manner on the individual trip level and can be well transformed to measure transit services of other cities.

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Section: 2mentioning

confidence: 72%

A Novel Trip Coverage Index for Transit Accessibility Assessment Using Mobile Phone Data

Cai

Wang

Chen

2017

Journal of Advanced Transportation

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“…The fluctuated transit demand and supply were considered in the gravity method to measure transit accessibility [26]. With travel time to the 17 destination types by transit and walking, the responding accessibility scores were attained according to the range of travel time and transformed it into public transit and walking accessibility index [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Feeder Bus Accessibility Modeling and Evaluation

et al. 2020

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To improve first/last mile travel services between metro stations and communities, this study modeled and analyzed four kinds of feeder bus operation strategies in terms of travel time and accessibility. The analytical modeling was used to compare the travel times and the simulation experiments were used to compare the accessibilities of different operation strategies. The results showed that when the ratio between length and width of study area increases, the number of stops for the fixed route transit with fixed stops will increase. When the travel demand is low, the demand responsive transit with separate routes has the highest accessibility. When the travel demand is high, the fixed route transit with fixed stops provides the highest accessibility. In addition, the ratio of flows in two passenger directions has different influences on the four operation strategies. This study can provide guidance for feeder bus operation to improve public transportation attraction.

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“…The development of the travel time cube stems from earlier research that brought to light the temporal variability that exists in transit-based access to destinations (Lei & Church, 2010;Lei, Chen, & Goulias, 2012;Polzin, Pendyala, & Navari, 2002) and the investigation of this temporal variability within a variety of applied accessibility studies (Farber et al, 2014;Owen & Levinson, 2015;Legrain, Buliung, & El-geneidy, 2015;Farber, Ritter, & Fu, 2016;Boisjoly & El-geneidy, 2016;Fransen et al, 2015;Xu, Ding, Zhou, & Li, 2015). All of these works use the variation in transit supply to measure accessibility changes over the course of the day.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic public transit accessibility using travel time cubes: Comparing the effects of infrastructure (dis)investments over time

Farber

2017

Computers, Environment and Urban Systems

123

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We put forward a new data object called the public transit travel time cube and demonstrate how the cube can be used in the analysis of transit travel time changes over space and time. The travel time cube contains the shortest path transit travel time between sets of origins and destinations in the city, at all times of day. Once computed, a wide range of investigations become readily available to the transit planner or transportation researcher. We conduct three demonstrative analyses using travel time cubes for the Wasatch Front, Utah and the Portland region in Oregon. Our studies investigate how travel times were impacted by service cuts and expansions in the two regions respectively and the impact this had on jobs accessibility. We also use the travel time cube to study the last mile problem, and compute the travel time savings and the stability gained by solving the last mile problem with bicycling. The paper concludes with an expanded discussion on the merits of the travel time cube and outlines four avenues for continued research.

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Transit accessibility measures incorporating the temporal dimension

Cited by 68 publications

References 40 publications

A Novel Trip Coverage Index for Transit Accessibility Assessment Using Mobile Phone Data

A Novel Trip Coverage Index for Transit Accessibility Assessment Using Mobile Phone Data

Feeder Bus Accessibility Modeling and Evaluation

Dynamic public transit accessibility using travel time cubes: Comparing the effects of infrastructure (dis)investments over time

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