Bicycle-Sharing System Analysis and Trip Prediction

Abstract

Abstract-Bicycle-sharing systems, which can provide shared bike usage services for the public, have been launched in many big cities. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a shortdistance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. Therefore, inferring the potential destinations and arriving time of each individual trip beforehand can effectively help the service providers schedule manual bike re-dispatch in advance. In this paper, we will study the individual trip prediction problem for bicycle-sharing systems. To address the problem, we study a real-world bicycle-sharing system and analyze individuals' bike usage behaviors first. Based on the analysis results, a new trip destination prediction and trip duration inference model will be introduced. Experiments conducted on a real-world bicycle-sharing system demonstrate the effectiveness of the proposed model.

Effects of Free-Floating Shared Bicycles on Urban Public Transportation

“…Therefore, studying the effects of shared bicycles on urban public transportation is an important research topic. The existing research qualitatively analyzed the great contribution of shared bicycles in relieving the pressure on public transportation systems [23][24][25]. Some scholars have analyzed the effects of public bicycle systems on the travel time of public transportation, and the results showed that public bicycle systems improved the efficiency of urban public transport trips [26][27][28].…”

Spatiotemporal analysis of urban mobility dynamics: a case study of bicycle sharing system

“…Each user may have unique movement styles and preferences that lead to diverse trip frequency, duration, speed, waiting times, motifs, distance, and direction. Meanwhile, the same regularities and patterns are likely to be associated with the same user type: commuters, casual users, tourists, and night workers [12,[71][72][73], for example. If the homogenous users can be grouped into certain clusters, it would be possible to measure cluster predictability level and use their collective trends to make a cluster-based prediction than using non-homogenous of whole users.…”

“…Other studied data sets are from Chicago [73,80,98,99], Lyon [63,65,74,100], Boston [12,76,80,101], Barcelona [11,70,102], Hangzhou [15,16,103], Brisbane [61,83], Minneapolis [76,104], Vienna [105,106], Denver [76,84], Pisa [64,107], Dublin [14,108], Minnesota [84], Seville [102], Montreal [109] Helsinki [110], Vancouver [111], Nanjing [112], and Castellon [113]. In addition to BSS data, some of those studies also used weather data as a feature of their analyses.…”

“…Similar to the work on human mobility prediction in subsection 2.1.7 above, numerous prediction methods have been applied to BSS data. They have been investigated for various prediction purposes such as prediction of traffic flows [15,17], the available of bikes and/or vacant docking slots [47,70], number of trips [16], trips duration [73], level of demand [62,85], over-demand prediction [81], pairwise demand prediction [96], number of usage patterns [27], next place prediction [46], cycle lane usage [109], station occupancy [120], the potential destination station and arrival time [73], waiting time for the next available bike [14], and pair of pickup and return stations that close to the origin and destination places of users [73,96].…”

“…Furthermore, the impact of human mobility analysis needs to be more widely applied to practical understanding of BSS system operation. Manhattan distance [73], which maps well to grids of roads that are aligned with the Manhattan distance axes, but gives poor estimates for roads that are not so aligned. Both of these distance measures give a poor estimate of inter-station travel time when there are obstacles between the stations, such a railway line or river that require a roundabout route between stations.…”