Understanding taxi drivers’ routing choices from spatial and social traces

Liu, Siyuan; Wang, Shuhui; Liu, Ce; Krishnan, Ramayya

doi:10.1007/s11704-014-4177-4

Cited by 21 publications

(17 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The proposed approach provides a path for a taxi while optimizing a certain cost function, such as traveled distance or gasoline consumption. In [32], the authors found out that driver's cruising choice is learned from his/her previous experience and his/her interactions with other drivers. In [33], the authors proposed pCruise system to reduce the taxi's cruising miles by providing the shortest cruising route with at least one expected available passengers for this route.…”

Section: Related Workmentioning

confidence: 99%

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

2020

View full text Add to dashboard Cite

Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers' quality of experience and drivers' benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases.Electronics 2020, 9, 648 2 of 24 a means of transportation and enhance the efficiency of both services for the benefits of both customers and drivers.In regular taxi services, traditional ways for taxi drivers to find potential customers include driving around the city and waiting at some 'hot spots', e.g., taxicab stands. For the first option, taxi drivers usually follow an intuition-based trajectory hoping to find customers as soon as possible, while for the second option, most of the drivers will target the same hot spots since based on their personnel experience, they know when and where customers will be gathered. In the latter case, regular taxi drivers may be subject to an unfair competition since the number of taxis is higher than the demand or vice versa. Hence, traditional solutions for customer hunting are usually exhaustive and inaccurate. On the other hand, for the ride-hailing taxi services, although a central server is dedicated to manage the requests of customers and allocate them to drivers, similar problems that face regular taxi services still exist. Customers' requests might still be raised far away from drivers' locations and high vacant distances are accumulated, resulting in huge and redundant fuel consumption. In Portland, the average waiting times are estimated to be around six and ten minutes for regular and ride-hailing taxi services, respectively, according KGW News [2]. Therefore, it is recommended to enhance the efficiency of such transportation services by tackling the offer/demand problem in both taxi categories.Thanks to the spread of on-board and infrastructure-based sensors [3], collecting and sharing data have become very com...

show abstract

Section: Related Workmentioning

confidence: 99%

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

2020

View full text Add to dashboard Cite

show abstract

“…To address the problem that noisy expert behaviors degrade the IRL performance significantly, a robust IRL framework was developed in [16]. Liu et al [17]- [19] proposed a novel, non-density-based approach called mobility-based clustering to perceive the vehicle crowdedness in nearby areas by using their instant mobility, and then they proposed a Gaussian Process Dynamic Congestion Model for analyzing rationality from trajectories in terms of a set of impact factors. A novel approach based on matrix completion (MC) was proposed to recover the successive missing and corrupted data in [20].…”

Section: B Related Workmentioning

confidence: 99%

Waiting or Moving? A Crossroad Network-Based Markov Decision Process Approach to Catch Vacant Taxis

Rong

Zhang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Taxi services play a critical role in the public transportation system in our cities. However, we usually find it difficult to catch vacant taxis based on our experience alone in the random taxi-waiting mode, especially on the streets unfamiliar to us, which may greatly influence users' taxi service experience. Therefore, how to recommend appropriate waiting locations for passengers becomes meaningful, and the available large-scale taxi trajectory data have helped with the right recommendation. Recent researches have focused on the one-location recommendation for the passengers without considering the recommendation failure situation where they find no vacant taxis available after waiting a long time. In response to this deficiency, we designed a Crossroad Network-based Markov Decision Process (CN-MDP) scheme to recommend a waiting location sequence for a current passenger whose cumulative probability of catching a vacant taxi getting close to 100%. Further, our scheme changes the recommended locations from the road segments to the crossroads, as we discovered that passengers are more likely to catch vacant taxis at the crossroads connected to multi-road segments. In addition, the multi-passengers competing strategy for vacant taxis at the same location and in the same time slot is also involved in our scheme by dynamically updating the pass rate of vacant taxis at each crossroad and road segment. Some evaluations on a real taxi data set from a major city in China have shown that our recommendation scheme works well and has a higher probability of catching vacant taxis than that of our previous approach and other ones. Our scheme further improves the user experience of taxi services. INDEX TERMS Markov decision process, waiting location sequence, pass rate of vacant taxis, multi-passengers competition, taxi services.

show abstract

“…Pick-up locations usually represent the hot spots where there are high taxi travel demands [2,21,28,32]. Thus, we can unite the historical pick-up information to explore high-demand areas and analyze the distribution patterns of pick-up hot spots.…”

Section: Cluster Of Pick-up Locationsmentioning

confidence: 99%

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

Yin

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.

show abstract

Understanding taxi drivers’ routing choices from spatial and social traces

Cited by 21 publications

References 16 publications

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

Waiting or Moving? A Crossroad Network-Based Markov Decision Process Approach to Catch Vacant Taxis

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

Contact Info

Product

Resources

About