Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments

Liu, Siyuan; Yue, Yisong; Krishnan, Ramayya

doi:10.1109/tkde.2015.2411278

Cited by 25 publications

(6 citation statements)

References 39 publications

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“…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. In [34], the authors developed efficient algorithms for non-myopic adaptive routing to minimize the collective travel time of all vehicles in the system. In [35], the authors proposed solutions to reduce the number of cruising miles while increasing the number of live miles of taxis by suggesting profitable locations to taxicab drivers.…”

Section: Related Workmentioning

confidence: 99%

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

2020

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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...

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Section: Related Workmentioning

confidence: 99%

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

2020

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“…Of particular importance in adaptive sampling methods is the choice of the acquisition function for evaluating candidate sample locations. Common approaches for this optimization criteria are based on entropy (Low et al, 2009), mutual information (Krause et al, 2008), or the combination of the predictive mean and information gain (Liu et al, 2015). Additionally, the work in Cao et al (2013) compares the effectiveness of entropy and mutual information criteria.…”

Section: Related Workmentioning

confidence: 99%

Adaptive sampling with an autonomous underwater vehicle in static marine environments

Stankiewicz

Tan

Kobilarov

2020

Journal of Field Robotics

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This paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point-source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real-time, for which standard (e.g., grid-based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions.

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“…Also, refueling is not considered for ICE taxis, because ICE taxi drivers normally fill up the gas tanks between the shifts 6 . Then the MDP model for an ICE taxi is identical to that of an electric taxi in Sec.…”

Section: A Basic Setting Of Ice Taximentioning

confidence: 99%

“…They also found that better taxi drivers can deliver the passengers efficiently by choosing a uncongested route. Furthermore, GPS mobility trace from taxis can be used to predict future traffic conditions and optimize the route selections [6]. Also, community detection has been applied to the mobility trace to reveal potential similar passengers' travel patterns, as for social recommendation [7] and improving transportation services [8].…”

Section: Background a Related Workmentioning

confidence: 99%

“…Note that if we only modify the behavior of a small number of taxis, then this simple approach will give rather accurate evaluation. Also, refueling is not considered for ICE taxis, because ICE taxi drivers normally fill up the gas tanks between the shifts 6 . Then the MDP model for an ICE taxi is identical to that of an electric taxi in Sec.…”

Section: A Basic Setting Of Ice Taximentioning

confidence: 99%

See 1 more Smart Citation

Improving Viability of Electric Taxis by Taxi Service Strategy Optimization: A Big Data Study of New York City

Tseng

Chau

Xue

2019

IEEE Trans. Intell. Transport. Syst.

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Electrification of transportation is critical for a lowcarbon society. In particular, public vehicles (e.g., taxis) provide a crucial opportunity for electrification. Despite the benefits of eco-friendliness and energy efficiency, adoption of electric taxis faces several obstacles, including constrained driving range, long recharging duration, limited charging stations and low gas price, all of which impede taxi drivers' decisions to switch to electric taxis. On the other hand, the popularity of ride-hailing mobile apps facilitates the computerization and optimization of taxi service strategies, which can provide computer-assisted decisions of navigation and roaming for taxi drivers to locate potential customers. This paper examines the viability of electric taxis with the assistance of taxi service strategy optimization, in comparison with conventional taxis with internal combustion engines. A big data study is provided using a large dataset of real-world taxi trips in New York City. Our methodology is to first model the computerized taxi service strategy by Markov Decision Process (MDP), and then obtain the optimized taxi service strategy based on NYC taxi trip dataset. The profitability of electric taxi drivers is studied empirically under various battery capacity and charging conditions. Consequently, we shed light on the solutions that can improve viability of electric taxis.

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Non-Myopic Adaptive Route Planning in Uncertain Congestion Environments

Cited by 25 publications

References 39 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

Adaptive sampling with an autonomous underwater vehicle in static marine environments

Improving Viability of Electric Taxis by Taxi Service Strategy Optimization: A Big Data Study of New York City

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