Taxi Travel Purpose Estimation and Characteristic Analysis Based on Multi-source Data and Semantic Reasoning — A Case Study of Beijing

Abstract

Abstract.Taxi is an important part of urban public transportation which meets the demands of special people to travel from door to door. Taxi trip characteristics are influenced by districts location and travel purposes. This paper extracts the time and location information of taxi alighting based on the taxi meter data and taxi GPS data. Based on the point-of-interest (POI) and searching popularity of the POI from online map data, this paper also utilizes the semantic reasoning methods to predict the purpose of taxi travels, and taxi trips are grouped into three most popular types: commuting travel, business travel and external travel. Then, multi-source data analysis models are proposed to calculate the characteristic parameters of taxis trips including average travel mileage, travel time, regional travel intensity of three types of taxi trips. The case study of Beijing selects three typical areas of different land use types. The analysis result shows that trip characteristics of different areas are various and the travel distance in resident area is shortest, and the travelers leave for external transportation hub usually have lower sensitivity to travel mileage; and the taxi travel mileage of business areas are almost same with resident areas, however the travel time is more longer. The analysis results of taxi trips characteristics of different areas revealed in this paper provide significant reference for acquiring the taxi travel demand and travel characteristics, the taxi stations planning, the estimation of the reasonable amount of the taxis and the operation of the intelligent Taxi Dispatching System (TDS).

Evaluating healthcare resource inequality in Beijing, China based on an improved spatial accessibility measurement

“…Taxis are an important part of urban public transportation, which meet the demands of people traveling door to door (Si, Weng, Chen, & Wang, 2014). According to a survey (Haynes, Jones, Sauerzapf, & Zhao, 2006), more than 80% of people traveled to hospital by car (including household cars and taxis).…”

Assessing Patient bypass Behavior Using Taxi Trip Origin–Destination (OD) Data

“…Taxi GPS data together with other data (e.g., POI data) can be used in many ways to detect people's activities and can be quickly reviewed. Two major types of identification are travel type (e.g., working, and shopping) from a macroscopic perspective [23,24] and destination location (including land use type [25] and specific POI [26,27]) from a microcosmic perspective. For specific location identification, Si et al [23] concretely predefined three types of POI (e.g., restaurants) and their corresponding travel patterns (e.g., dining) before determining the candidate drop-off POI; then, the weight of each type of POI was calculated by considering the probability distribution of the travel pattern for different periods of time.…”