A Dynamic Routing Framework for Shared Mobility Services

Guan, Yue; Annaswamy, Anuradha M.; Tseng, Hung-Wei

doi:10.1145/3300181

Cited by 10 publications

(18 citation statements)

References 26 publications

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“…In this paper, we have proposed a dynamic pricing strategy for a SMoDS using Cumulative Prospect Theory, and builds on our previous work in [13] and [5]. The proposed dynamic pricing strategy together with dynamic routing via the AltMin algorithm [13], provide a complete solution to shared mobility on demand that corresponds to an ideal combination of flexibility, convenience, and affordability. The basic principles of CPT and the derivation of the passenger behavioral model in the SMoDS context were described in detail.…”

Section: Discussionmentioning

confidence: 99%

“…The optimization procedure is carried out iteratively by determining a set of routing points through which the vehicle picks up and/or drops off passengers, and the sequence at which these routing points are visited. It has been demonstrated in [13] that the AltMin algorithm is capable of accommodating real time requests, and outperforms standard Mixed Integer Quadratically Constrained Programming based approaches with an order of magnitude improvement in computational efficiency and with comparable optimality.…”

Section: Dynamic Routing Via Altmin Algorithmmentioning

confidence: 99%

“…A dynamic routing problem of sixteen passengers using real request data from San Francisco was considered (see [5] for details), and the request from the 6 th passenger was used for the computational experiments in this section. The AltMin algorithm developed in [13] was applied to derive the route and therefore the corresponding travel times of the SMoDS. The constraints on the possible delay were set to be at most 4 minutes of extra waiting and riding, respectively.…”

Section: Determination Of Parametersmentioning

confidence: 99%

See 2 more Smart Citations

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Guan

Annaswamy

Tseng

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Cumulative Prospect Theory (CPT) is a modeling tool widely used in behavioral economics and cognitive psychology that captures subjective decision making of individuals under risk or uncertainty. In this paper, we propose a dynamic pricing strategy for Shared Mobility on Demand Services (SMoDSs) using a passenger behavioral model based on CPT. This dynamic pricing strategy together with dynamic routing via a constrained optimization algorithm that we have developed earlier, provide a complete solution customized for SMoDS of multi-passenger transportation. The basic principles of CPT and the derivation of the passenger behavioral model in the SMoDS context are described in detail. The implications of CPT on dynamic pricing of the SMoDS are delineated using computational experiments involving passenger preferences. These implications include interpretation of the classic fourfold pattern of risk attitudes, strong risk aversion over mixed prospects, and behavioral preferences of self reference. Overall, it is argued that the use of the CPT framework corresponds to a crucial building block in designing socio-technical systems by allowing quantification of subjective decision making under risk or uncertainty that is perceived to be otherwise qualitative.our earlier work in [13] and [5], and offer a solution based on Cumulative Prospect Theory for determining dynamic tariffs.The results of [13] correspond to designing dynamic routes for passengers who request the SMoDS, based upon the requested pickup, drop-off locations, and a pre-specified bound on the walking distance by each passenger. An Alternating Minimization (AltMin) based algorithm was presented that optimizes a relevant time cost. The SMoDS server then offered pickup and drop-off locations as well as walking, waiting and riding times to each passenger derived via the AltMin algorithm. The notion of Transactive Control in [5] was introduced to enable the SMoDS to offer a dynamic tariff to the passenger which can serve as an incentive for the decision on the offer. A passenger behavioral model based on Utility Theory [22] was derived, with the utility of the passenger being a function of both travel times and tariff. The resulting socio-technical model that combines the passenger behavioral model and the optimization of dynamic routes was used to derive a desired probability of acceptance that led to the average estimated waiting time of passengers on the SMoDS platform being regulated around a desired value. The derivation of the actual dynamic tariffs was however not addressed and assumed to be such that the desired probability of acceptance from each passenger was realized.The results mentioned above have two deficiencies. The first is that the passenger behavioral model is significantly more complex than that considered in [5]. Strategic decision making, adjustments based upon framing effect, loss aversion, and probability distortion are several key features related to subjective decision making of individuals when facing uncertainty, which makes classic ...

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Section: Discussionmentioning

confidence: 99%

Section: Dynamic Routing Via Altmin Algorithmmentioning

confidence: 99%

Section: Determination Of Parametersmentioning

confidence: 99%

See 1 more Smart Citation

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Guan

Annaswamy

Tseng

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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“…c l e m s o n . e du/ mediarelations/so-long-redlights-controller-will-help-carspass-intersections-withoutstopping/), [6] (https://www .bu.edu/eng/2016/11/15/aninternet-of-cars/) •ve h icle sharing [9] (http:// feimiao.org/research.html), [14] •data analytics and optimization [33] (https://christosgcassandras .org/data-analytics-and-networkoptimization/). » Smart environments: In this area of research, the aim is for the effective management and optimization of precious resources and the encouragement of sound decisions by all players.…”

Section: Research Activitiesmentioning

confidence: 99%

Technical Committee on Smart Cities [Technical Activities]

2019

IEEE Control Syst.

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“…This manuscript pertains to an SMoDS solution that consists of customized dynamic routing and dynamic pricing. We build on our earlier works in [6,7,1] and propose an Markov Decision Process (MDP) formulation and a Dynamic Programming (DP) planning algorithm towards dynamic pricing for the SMoDS platform.…”

Section: Introductionmentioning

confidence: 99%

Towards Dynamic Pricing for Shared Mobility on Demand using Markov Decision Processes and Dynamic Programming

Guan

Annaswamy

Tseng

2019

Preprint

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In a Shared Mobility on Demand Service (SMoDS), dynamic pricing plays an important role in the form of an incentive for the decision of the empowered passenger on the ride offer. Strategies for determining the dynamic tariff should be suitably designed so that the incurred demand and supply are balanced and therefore economic efficiency is achieved. In this manuscript, we formulate a discrete time Markov Decision Process (MDP) to determine the probability desired by the SMoDS platform corresponding to the acceptance rate of each empowered passenger at each state of the system. We use Estimated Waiting Time (EWT) as the metric for the balance between demand and supply, with the goal that EWT be regulated around a target value. We then develop a Dynamic Programming (DP) algorithm to derive the optimal policy of the MDP that regulates EWT around the target value. Computational experiments are conducted that demonstrate the regulation of EWT is effective, through various scenarios. The overall demonstration is carried out offline. The MDP formulation together with the DP algorithm can be utilized to an online determination of the dynamic tariff by integrating with our earlier works on Cumulative Prospect Theory based passenger behavioral modeling and the AltMin dynamic routing algorithm, and form the subject of future works.

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A Dynamic Routing Framework for Shared Mobility Services

Cited by 10 publications

References 26 publications

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Cumulative Prospect Theory Based Dynamic Pricing for Shared Mobility on Demand Services

Technical Committee on Smart Cities [Technical Activities]

Towards Dynamic Pricing for Shared Mobility on Demand using Markov Decision Processes and Dynamic Programming

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