Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Wu, Liang; Hu, Sheng; Yin, Li; Wang, Shaobin; Chen, Zhanlong; Guo, Mingqiang; Chen, Hao; Xie, Zhong

doi:10.3390/ijgi6110373

Cited by 20 publications

(6 citation statements)

References 51 publications

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“…Cabs needs to be tracked, in real-time to improve their services. It may require to answer closest taxi to a calling customer; how many taxi's in proximity to a client in next 10 minutes [9] etc. these questions are not easily answered in available applications handling spatiotemporal data.…”

Section: Transportationmentioning

confidence: 99%

Spatiotemporal Applications of Big Data

Muhammad¹,

Soomro²

2018

IJCA

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Big data has emerged as a field of study and gained huge importance these days for both industry and researcher's point of view. Initially database management systems (DBMS) developed to solve data management and relevant queries. Relational DBMS (RDBM) gave another innovation to the database field. Through the passage of time, it observed that some issues remained unsolved and required some more dimensions be added to the data. One of those was time and the other was location. Spatiotemporal aspects of data gained importance and scientists thought of incorporating these in the upcoming databases. This paper covers the inclusion of these dimensions in a database and its applications in today's world. It also compares some of the tools used these days and suggests a combination for better results in an efficient and cost-effective way.

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Section: Transportationmentioning

confidence: 99%

Spatiotemporal Applications of Big Data

Muhammad¹,

Soomro²

2018

IJCA

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“…Zhang et al [35] proposed an impulse response function based on the vector autoregression model to provide insight into the cross effects of the traffic parameters and their responses to weather conditions. However, although many researchers have carried out extensive studies on other aspects of taxis [36][37][38][39][40][41][42][43], very few studies have dealt with the time lag effect of snowfall on normal taxi operations. As an important component of urban transportation system, the service level of taxi needs to be paid enough attention, especially in snowy days, where people's tolerance to low temperature is reduced and buses become unreliable and unpunctual.…”

Section: Introductionmentioning

confidence: 99%

How Snowfalls Affect the Operation of Taxi Fleets? A Case Study from Harbin, China

Li¹,

Cai

Dan

2022

Journal of Advanced Transportation

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Taxi network plays an important role in urban passenger transportation. However, its operation is greatly affected by weather, especially by snowfalls in cold region. In this study, we focus on the persistent effect of snowfall on taxi operation and propose an autoregressive distribution lag model (ARDL) to quantitatively analyse it. To support our study, the taxi GPS trajectory data collected in Harbin, China, during 61 days from 1 November to 31 December in 2015 is analysed. First, the daily average order volume (DAOV) is acquired through data sampling and processing. Then, combined with the data of daily snowfall during the 61 days, the ARDL model is constructed. The result shows that the snowfall has a lag effect on taxi operation and it lasts about 3 days. To better interpret the result, visualization of total 6 days before and after a heavy snowfall is conducted. The result also indicates that weekends have a positive effect on operation. These results are expected to assist us to better understand the effect of snowfall on taxi operation and provide some policy suggestions for local municipal and transportation management departments to ensure the normal operation of taxi networks.

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“…The model included picking the passengers, driving with or without a passenger and parking [21]. The distance between leaving a passenger and picking up another one, in other words, the rate of undertaking the next job, was also examined [22]. Empirical considerations of the way drivers look for their next passenger can also be found in the literature [23].…”

Section: Introductionmentioning

confidence: 99%

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

Król

2019

Sustainability

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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.

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Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model

Cited by 20 publications

References 51 publications

Spatiotemporal Applications of Big Data

Spatiotemporal Applications of Big Data

How Snowfalls Affect the Operation of Taxi Fleets? A Case Study from Harbin, China

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

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