Research on taxi drivers' passenger hotspot selecting patterns based on GPS data: A case study in Wuhan

Liu, Liqun; Wu, Chaozhong; Zhang, Hui; Hasan, Abid; Chu, Wenhui; Atombo, Charles

doi:10.1109/ictis.2017.8047802

Cited by 7 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The areas with flow clusters in Fig. 9 are also observed in existing studies (Liqun et al 2017; Naji, Wu, and Zhang 2017), in which those areas are identified as hotspots where taxi drivers tend to stop and wait for passengers. By comparing the results of K net (Fig.…”

Section: Resultssupporting

confidence: 77%

Ripley’s K‐function for Network‐Constrained Flow Data

Kan

Kwan

Tang

2021

Geographical Analysis

View full text Add to dashboard Cite

Many types of spatial flows, including pedestrian flows and vehicle flows, are constrained by and distribute on spatial networks. In the literature, network‐constrained flows are usually modeled as a direct line in planar space using methods designed for flows in planar space. Further, in spatial statistical analysis of flow patterns, distance measures and the hypothesis of spatial randomness of flows also have a significant impact on the determination of flow patterns. In this study, we extend the global and local Ripley’s K functions for planar flows to network space. Both the network and planar K‐functions for flows are applied to detect the patterns of taxi Origin‐Destination flow data on a road network at multiple scales. The effect of distance measures and simulation methods in the network and planar Ripley’s K functions are examined. We found that the planar K function is more sensitive to the changes in scale and tends to detect more clustered flows compared with the network K function at the same scale. Distance measures and simulation methods have a more significant influence on the detection of patterns of network‐constrained flows than the selection of the network or planar Ripley’s K functions. This study suggests that distance measures and hypotheses of spatial randomness have to be chosen carefully before applying flow pattern analytic methods to network‐constrained flows and interpreting the results of flow patterns.

show abstract

Section: Resultssupporting

confidence: 77%

Ripley’s K‐function for Network‐Constrained Flow Data

Kan

Kwan

Tang

2021

Geographical Analysis

View full text Add to dashboard Cite

show abstract

“…(1) Constructing spatio-temporal contexts. Firstly, the urban area is divided into the n*n regular grids [54] and the POI data of the city is classified into twelve types. Secondly, the GPS data are traversed and then the pick-up points are detected by computing the staying time of taxis.…”

Section: The Framework Of Stc_deepfmmentioning

confidence: 99%

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Liu¹,

Qing²,

Zhao³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads. But how to alleviate sparsity and further enhance accuracy is still challenging. Addressing at these issues, we propose to fuse spatio-temporal contexts into deep factorization machine (STC_DeepFM) offline for pick-up area recommendation, and within the area to recommend pick-up points online using factorization machine (FM). Firstly, we divide the urban area into several grids with equal size. Spatio-temporal contexts are destilled from pick-up points or points-of-interest (POIs) belonged to the preceding grids. Secondly, the contexts are integrated into deep factorization machine (DeepFM) to mine high-order interaction relationships from grids. And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation. Thirdly, we devise the architecture of offline-to-online (O2O) recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency. Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts, different recommendation models, and the O2O architecture. The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods, and the O2O architecture achieves excellent real-time performance.

show abstract

“…In many cities, however, people who want to demand a taxi are accustomed to parking an empty taxi by "roadside beckoning". As a result, the areas where taxi drivers receive their passengers are exceptionally arbitrary [3,4]. Both those who want to get a taxi and taxi drivers do not have enough data about a better area, which makes it surprising that a taxi driver experiences difficulties in locating passengers, while people think it is not easy to find an empty taxi.…”

Section: Introductionmentioning

confidence: 99%

Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

Sarooraj¹,

Shyry²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

In Intelligent Transportation Systems (ITS), controlling the traffic flow of a region in a city is the major challenge. Particularly, allocation of the trafficfree route to the taxi drivers during peak hours is one of the challenges to control the traffic flow. So, in this paper, the route between the taxi driver and pickup location or hotspot with the spatial-temporal dependencies is optimized. Initially, the hotspots in a region are clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to find the hot spots at the peak hours in an urban area. Then, the optimal route is allocated to the taxi driver to pick up the customer in the hotspot. Before allocating the optimal route, each route between the taxi driver and the hot spot is mapped to the number of taxi drivers. Among the map function, the optimal map is selected using the rain optimization algorithm (ROA). If more than one map function is obtained as the optimal solution, the map between the route and the taxi driver who has done the least number of trips in the day is chosen as the final solution This optimal route selection leads to control of the traffic flow at peak hours. Evaluation of the approach depicts that the proposed traffic flow control scheme reduces traveling time, waiting time, fuel consumption, and emission.

show abstract

Research on taxi drivers' passenger hotspot selecting patterns based on GPS data: A case study in Wuhan

Cited by 7 publications

References 16 publications

Ripley’s K‐function for Network‐Constrained Flow Data

Ripley’s K‐function for Network‐Constrained Flow Data

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Optimal Routing with Spatial-Temporal Dependencies for Traffic Flow Control in Intelligent Transportation Systems

Contact Info

Product

Resources

About