Measuring Taxi Accessibility Using Grid-Based Method with Trajectory Data

Abstract

Accessibility has drawn extensive attention from city planners and transportation researchers for decades. With the benefits of large-scale and varying time, this study aims to combine the taxi global positioning system (GPS) data with a cumulative opportunity measure to calculate taxi accessibility in Beijing, China. As traffic conditions vary significantly over time and space, we select four typical time periods and introduce a grid-based method to divide the study area into grid cells. Both the GPS signals and opportunities that include the constant points of interest, total drop-offs, and dynamic drop-offs, are aggregated in these grid cells. The cumulative opportunity measure counts all reachable grid cells within the given travel time threshold, along with the corresponding opportunities. The results demonstrate that the accessibility varies in the four time periods, with better performance seen in the late-night hours. Although the spatial distributions of the three kinds of opportunities are different, these accessibilities show great similarity. In addition, the relative accessibilities of different measures are highly correlated. In general, grid cells with higher accessibilities in one time period are likely to also have higher accessibilities in other time periods. Moreover, the results suggest that taxi accessibility can be measured from its trajectory data only.

Demand Responsive Service-based Optimization on Flexible Routes and Departure Time of Community Shuttles

“…where C T is the total cost, which includes both the operator cost and the passengers' in-vehicle cost. C S is the operator cost, which is related to the set of designed routes (S) and can be further expressed as (2), and C I is the passenger in-vehicle cost, which is related to both S and the set of vehicle departure time (denoted by D 0 ) and is further expressed as (3).…”

“…Community shuttle services provide flexible mobility to public transit passengers dispersing into a large community (especially in suburbs) to access nearby metro stations. However, due to the extremely uneven temporal distribution of the trip demands [1][2][3], shuttle operators have to significantly lower the service frequency or even suspend the service during the nonpeak hours. This situation usually occurs in suburban communities in some populous huge cities, such as Tiantongyuan and Huilongguan Communities in Beijing, China.…”

Analyses of the Imbalance of Urban Taxis’ High-Quality Customers Based on Didi Trajectory Data

“…We then divide the grid size as 500m×500m and 1km×1km, respectively, and reach some similar conclusions. Referring to previous partition methods of grid [36,[38][39][40], we take the grid size of 1km×1 km as an example to describe the results (please refer to the Appendix Figures 7 and 8 for the details of results as per taking the grid size as 500m×500m). Consequently, Beijing is divided into 31,150 latitude and longitude grids (the latitude span of each grid is 0.009 ∘ and the longitude span is 0.01174 ∘ ).…”

Automatic Identification of the Social Functions of Areas of Interest (AOIs) Using the Standard Hour-Day-Spectrum Approach

“…Apart from the abovementioned data sets, travel data such as currency data [28], mobile phone data [29,33], subway bus card data [34] and floating car data [35,36] are widely used in the analysis of travel behaviors. Among these, the trajectory data of taxicabs record the spatial location more closely to the destination, which is more suitable for fine-grained research.…”