Examining the Interaction of Taxi and Subway Ridership for Sustainable Urbanization

The success of metro systems depends on effective multimodal solutions that bridge the first-and-last-mile gaps. Both dockless bike sharing (DBS) and taxis are important feeder modes for metros, which provide on-demand travel options with high flexibility and accessibility. Based on one-week trip data of DBS and taxis during a concurrent period in Beijing, China, the paper aims to compare the temporal-spatial distribution of two modes as first-and-last-mile connectors and find out the socio-demographic and built-environment factors that impact their usage. K-means clustering is implemented to visualize the spatial distribution of DBS and taxis around metro stations, and the spatial lag model incorporating spatial autocorrelations of variables is developed. The results show that people prefer to use DBS as a substitutable mode for bus services to serve first-mile interchange in the morning. Also, less economically developed areas with a high density of branches and fewer signalized intersections are more favored by DBS users, whereas people in the central areas with high housing price and developed arterial road network tend to take a taxi, especially during evening peak period. The study can offer the policy guidance to improve DBS services, and several recommendations are suggested to ensure the sustainable development of DBS.

Section: Introductionmentioning

confidence: 99%

Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis

Chen

2020

“…Taxis, as a supplement of public transportation, are an important option for individual travel in big cities. Understanding people's movement patterns from their taxi trips can promote sustainable mobility and urban transportation through improving taxi services, taxi sharing, transportation planning, and so on [1][2][3][4][5]. GPS sensors equipped in taxicabs continuously record their locations, and subsequently generate a vast amount of trajectory data.…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Constraint Non-Negative Matrix Factorization Model to Discover Intra-Urban Mobility Patterns from Taxi Trips

Gao

Liu

et al. 2019

Taxi services provide an urban transport option to citizens. Massive taxi trajectories contain rich information for understanding human travel activities, which are essential to sustainable urban mobility and transportation. The origin and destination (O-D) pairs of urban taxi trips can reveal the spatiotemporal patterns of human mobility and then offer fundamental information to interpret and reform formal, functional, and perceptual regions of cities. Matrices are one of the most effective models to represent taxi trajectories and O-D trips. Among matrix representations, non-negative matrix factorization (NMF) gives meaningful interpretations of complex latent relationships. However, the independence assumption for observations is violated by spatial and temporal autocorrelation in taxi flows, which is not compensated in classical NMF models. In order to discover human intra-urban mobility patterns, a novel spatiotemporal constraint NMF (STC-NMF) model that explicitly solves spatial and temporal dependencies is proposed in this paper. It factorizes taxi flow matrices in both spatial and temporal aspects, thus revealing inherent spatiotemporal patterns. With three-month taxi trajectories harvested in Beijing, China, the STC-NMF model is employed to investigate taxi travel patterns and their spatial interaction modes. As the results, four departure patterns, three arrival patterns, and eight spatial interaction patterns during weekdays and weekends are discovered. Moreover, it is found that intensive movements within certain time windows are significantly related to region functionalities and the spatial interaction flows exhibit an obvious distance decay tendency. The outcome of the proposed model is more consistent with the inherent spatiotemporal characteristics of human intra-urban movements. The knowledge gained in this research would be useful to taxi services and transportation management for promoting sustainable urban development.Sustainability 2019, 11, 4214 2 of 22 knowledge [6], including human mobility patterns [7,8], driving strategies of cabdrivers for seeking passengers [9][10][11][12], and urban spatial structures [13][14][15], for understanding human mobility and even improving sustainable urban transport.A taxi trip depends on the specific travel purpose. The origin and destination (O-D) points of a trip are considered the critical elements to represent mobility motions, while the route itself is less semantically representative for passengers. Generally, an O-D pair is exactly the pair of pick-up and drop-off points of a taxi ride, which represents the starting and stopping locations of a complete individual trip trajectory. O-D flows generalize not only trajectory geometries but also mobility semantics. Thus, previous studies have explored them to understand human mobility and urban structures. Direction and distance distributions of taxi trips were investigated to model the geographical heterogeneity and distance decay effect of intra-urban human mobility [8]. Mobility hotspots [16], movemen...

“…Taxis provide promptness, door-to-door service, privacy, comfort, high coverage of the city space and all day availability day time, and nowadays are an inevitable part of the communication system of any big city. This is due to the above-mentioned advantages, despite the higher cost of traveling [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A Stochastic Simulation Model for the Optimization of the Taxi Management System

Król

2019

The paper presents a simplified simulation model of the operation of a taxi system. The model retains the main features of a real taxi transportation system and despite its simplicity examines the system behavior in different conditions. It was shown that for every request generation rate a critical number of taxis in disposal could be determined. If the real number of taxis is lower than the critical number, the queue of pending requests grows in an unlimited way. On the other hand, if the real number of taxis is significantly higher, the service level is clearly not better and leads to the waste of taxi drivers' time and fuel. The presented model can be regarded as a queue system; therefore, the well-known queue theory is used to describe its nature. However, this approach has some practical limitations coming from incomplete knowledge on real transportation demands, which additionally undergo significant fluctuations. A method, which optimizes the assignment of vacant taxis to the pending requests was also introduced. It was proven that this method mitigated the influence of the above-mentioned limitations.