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

Abstract

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.

A conceptual framework for incorporating competitiveness into network-level transit quality metrics

“…If we truly want to design a network that encourages mode shift for both environmental and economic reasons, we should evaluate our networks on their competitiveness, since mode shift from auto-based travel is expected to occur more frequently when transit is a viable option for trip-making ( Koenig, 1980 , Ellison and Greaves, 2011 ). Service planners often design routes with competitiveness in mind ( Smith et al, 2005 ), but this approach has not generally been incorporated into network level measures (with the notable exception of one recent attempt, Cai et al, 2017 ). Competitiveness, in this framework, is what we are using to define high-quality transit.…”

“…Additionally, when competitiveness between transit and auto-based modes is used as a measure of transit quality, often travel time used as the only point of comparison ( Cai et al, 2017 , Arellano Associates, 2019 ). However, multiple factors can and do play a part in mode choice; number of transfers, frequency, and reliability of service come up often in individual choice research.…”

“…The largest impact of this decision is likely to come from the threshold of a minimum distance that makes a trip “count” in the universe of trips for which good transit needs to be provided. For example, Cai et al, 2017 , sets the minimum threshold at double the expected walk distance to access service (they use 1000 m as the minimum distance for trips that “count” in the denominator). This is likely to vary due to regional factors like overall density, sidewalk coverage, and other factors that make walking a more or less reasonable decision for short trips.…”

“…A recent exception in this trend is the ongoing Los Angeles bus network redesign, which has been based on the competitiveness of transit versus auto-based modes ( Arellano Associates, 2019 ), and some research work is moving in this direction (e.g. Cai et al, 2017 ). The proposed framework takes the trip-level concept and aggregates it to the network level, where the transit network is evaluated for all of the trips made in the region.…”

Development of a Model for Evaluating the Coverage Area of Transit Center Using Smart Card Data

“…ere was another approach using mobile phone data to get the origin and destination of trips with unknown modes. e origins and destinations, travel time, and distance of the private car and public transit were compared to define the accessibility index [23]. ese studies presented some ways to assess accessibility by datadriven approach, but they did not use the origin and destination of transit trips.…”