Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure

Multi-attribute taxi logistics optimization

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2006.Includes bibliographical references (leaves 102-103).According to U.S. government surveys, 12% of Americans used taxi service in the previous month' and spent about $3.7 billion a year for cab fare.2 Taxi service is one of the major modes of public transportation. Despite providing services 24 hours a day, driving relentlessly with an empty taxicab in search of passengers and answering dispatch calls instantaneously, taxi service is ranked the most unsatisfactory mode of transportation by the public. Charging higher fares than other major modes of transportation and averaging 10 to 12 hours work day, taxi drivers have a difficult time to earn a sustainable income.Approximately half of all the taxi mileage is paid mileage; this means a significant portion of a taxi's time and fuel is spent on non-revenue generating activities, i.e. without passengers. Current taxi allocation is inefficient. The number of taxis and the geographical service areas which they serve are heavily regulated in most cities. With limited competition and strict regulations, taxi service suffers with customers having to endure long wait times and inferior services. The current taxi systems in most U.S. cities may be greatly improved from their current state.(cont.) This thesis investigates the factors of inefficiency in the current taxi system, reviews previous taxi efficiency studies, and suggests possible solutions. After extensive literature reviews and field research, a computer simulation model has been built in the MATLAB environment. This computer model tests various attributes that affect logistic optimizations for taxi services. In particular, the effect of taxi fleet size, the quantity of hotspots, and the concentrations of customers at hotspots are analyzed in detail using the model. The metric of interest includes the customers' wait time, taxi revenue, and costs of operations. Results from the computer simulation experiments, field research, and literature review are analyzed and synthesized. Possible solutions are proposed as part of this thesis.by Sonny Li.S.M

Information Effect in Taxi Service Double Auction with Opportunity Cost: An Experimental Analysis

Inspired by the taxi refusal problem, we propose a double auction for taxi services with a variable supply-and-demand fare surcharge. In contrast to a conventional commodity double auction, our taxi service auction involves an opportunity cost that generally increases with time. Therefore, the bidding behaviour differs from that reported in the literature. We investigate experimentally the effects on bidding behaviour of the opportunity costs and private values of drivers and passengers, as well as of the types of given information. In addition, we compare the realized benefits between passengers and drivers. The results show that the degree of sensitivity to private value differs with opportunity cost and that transaction statistics vary with types of the information given to bidders. Moreover, we found that providing more information to bidders does not necessarily lead to better market performance and that giving suitable information to bidders could lead to a balanced benefit between passenger and driver.Inspired by the taxi refusal problem, we propose a double auction for taxi services with a variable supply-and-demand fare surcharge. In contrast to a conventional commodity double auction, our taxi service auction involves an opportunity cost that generally increases with time. Therefore, the bidding behaviour differs from that reported in the literature. We investigate experimentally the effects on bidding behaviour of the opportunity costs and private values of drivers and passengers, as well as of the types of given information. In addition, we compare the realized benefits between passengers and drivers. The results show that the degree of sensitivity to private value differs with opportunity cost and that transaction statistics vary with types of the information given to bidders. Moreover, we found that providing more information to bidders does not necessarily lead to better market performance and that giving suitable information to bidders could lead to a balanced benefit between passenger and driver

A duopoly of transportation network companies and traditional radio-taxi dispatch service agencies

Transportation network companies commonly enter the market for taxi ride intermediation and alter the market outcome. Compared to cooperatively organized radio-taxi dispatch service agencies, transportation network companies run larger fleets and serve more customers with lower fares, when the fixed costs of the dispatch office are relatively small. The same holds for private dispatch firms, when the fixed costs of a taxicab are not too small. These results are shown in a two-stage duopoly of fare and fleet size competition with fare- and waiting-timedependent demand

Extending Kolkata Paise Restaurant Problem to Dynamic Matching in Mobility Markets

In mobility markets – especially vehicle for hire markets – drivers offer individual transportation by car to customers. Drivers individually decide where to go to pick up customers to increase their own utilization (probability of carrying a customer) and utility (profit). The utility drivers retrieve from customers comprises both costs of driving to another location and the revenue from carrying a customer and is thus not shared between different drivers. In this thesis, I present the Vehicle for Hire Problem (VFHP) as a generalization of the Kolkata Paise Restaurant Problem (KPRP) to evaluate different strategies for drivers in vehicle for hire markets. The KPRP is a multi-round game model presented by Chakrabarti et al. (2009) in which daily laborers constitute agents and restaurants constitute resources. All agents decide simultaneously, but independently where to eat. Every restaurant can cater only one agent and agents cannot divert to other resources if their first choice is overcrowded. The number of agents equals the number of resources. Also, there is a ranking of restaurants all agents agree upon, and no two resources yield the same utility. The VFHP relaxes assumptions on capacity and utility: Resources (customers) are grouped in districts, agents (drivers) can redirect to other resources in the same district. As the distance between agent and resource reduces the agent’s utility and the location is not identical for all agents, the utility of a given resource is not identical for all agents. To study the impact of the different assumptions, I build four different model variants: Individual Preferences (IP) replaces the shared utility of the KPRP with uniformly distributed utilities per agent. The Mixed Preferences (MP) model variant uses the utility assumption of the VFHP, but the capacity of all districts remains 1. The Individual Preferences with Multiple Customers per District (IPMC) model variant groups customers in districts, and uses the uniform utilities introduced in the IP model variant. Mixed Preferences and Multiple Customers per District (MPMC) implements all assumptions of the VHFP. In this thesis, I study different strategies for the KPRP and all variants of the VFHP to build a foundation for an incentive scheme for dynamic matching in mobility markets. The strategies comprise history-dependent and utility-dependent strategies. In history-dependent strategies, agents incorporate their previous decisions and the utilization of resources in previous iterations in their decision. Agents adapting utility-dependent strategies choose the resource offering the highest utility with a given probability. Keywords: vehicle for hire markets; distributed decision making; agent-based modelling; congestion game; limited rationalit

A multiperiod dynamic model of taxi services with endogenous service intensity

This paper presents a spatially aggregated multiperiod taxi service model with endogenous service intensity. The whole day service period is divided into a number of subperiods; during each subperiod, taxi supply and customer demand characteristics are assumed to be uniform. Customer demand is period-specific and described as a function of waiting time and taxi fare. Taxi operating cost for each work shift consists of two components: one component a function of total service time and the other component period-dependent. Each taxi driver can work for one or more shifts each day and freely chooses the starting and ending time of each shift. Equilibrium of taxi services is obtained when taxi drivers cannot increase their individual profits by changing their individual working schedules. A novel clock network representation is proposed to characterize the multiperiod taxi service equilibrium problem. The problem of interest is formulated as a network equilibrium model with path-specific costs and arc-capacity constraints, which can be solved using conventional nonlinear network flow optimization methods. The proposed model can ascertain at equilibrium the service intensity and utilization rate of taxis and the level of service quality throughout the day. The information obtained is useful for the prediction of the effects of alternative government regulations on the equilibrium of demand and supply in the urban taxi industry. © 2005 INFORMS.link_to_subscribed_fulltex

Economic Perspectives on Automated Demand Responsive Transportation and Shared Taxi Services - Analytical models and simulations for policy analysis

The automated demand responsive transportation (DRT) and modern shared taxi services provide shared trips for passengers, adapting dynamically to trip requests by routing a fleet of vehicles operating without any fixed routes or schedules. Compared with traditional public transportation, these new services provide trips without transfers and free passengers from the necessity of using timetables and maps of route networks. Furthermore, automated DRT applies real-time traffic information in vehicle routing and in formulating trip offers with travel time promises, which enables differentiated pricing based on travel times and thereby tailored service provision for personal passenger needs.  This work considers the potential economic impacts of automated DRT and shared taxi services on urban transportation, and explores the effects of various transport policies on these new transport services as an integral part of urban transportation system. Analytical models are presented to define welfare optimal policies for these services, which have different trip production cost structures and external costs compared to conventional bus and taxi services. Moreover, simulation models are developed to analyse cost-effectiveness and regulation policies.  Furthermore, alternative pricing models for these services are analysed from the viewpoint of transport companies, passengers and transport policy.  The publications presented in this dissertation provide theoretical foundations, models and insights based on empirical data for further policy analyses and empirical research on automated DRT and shared taxi markets. These markets are evolving due to the advances in intellingent transportation technologies adopted by innovative and even revolutionary companies, and due to the increasing political pressures for sustainable transportation.Automatisoitu kysyntäohjautuva joukkoliikenne ja modernit jaetut taksipalvelut tarjoavat jaettuja kyytejä matkustajille makautuen dynaamisesti matkapyyntöihin reitittämällä operoivat ajoneuvot reaaliaikaisesti ilman ennalta määrättyjä reittejä ja aikatauluja. Verrattuna perinteiseen julkiseen liikenteeseen nämä uudet liikennepalvelut tarjoavat matkoja ilman vaihtoja ja vapauttavat matkustajat käyttämästä aikatauluja ja linjastokarttoja. Tämän lisäksi automatisoitu kysyntäohjautuva joukkoliikenne hyödyntää reaaliaikaista liikennetietoa ajoneuvojen reitityksessä ja muodostaessaan matka-aikalupauksia sisältäviä matkatarjouksia, mikä mahdollistaa matka-aikoihin perustuvan hintadifferoinnin ja siten yksilöllisemmän palvelutarjonnan matkustajien tarpeisiin.  Tämä työ tarkastelee automatisoidun kysyntäohjautuvan joukkoliikenteen ja jaettujen taksipalvelujen potentiaalisia vaikutuksia urbaanille liikenteelle sekä tutkii erilaisten liikennepolitiikkojen vaikutusta näihin palveluihin kiinteänä osana urbaania liikennesysteemiä. Työssä esitetään analyyttisiä malleja, joilla määritellään yhteiskunnan hyvinvoinnin optimoivia politiikkoja näille palveluille, joilla on perinteisiin bussi- ja taksipalveluihin verrattaessa erilaiset palvelutuotannon kustannusrakenteet sekä ulkoiskustannukset. Lisäksi työssä kehitetään simulointimalleja kustannustehokkuuden ja säännöstelypolitiikkojen analysointiin. Tämän lisäksi vaihtoehtoisia hinnoittelumalleja tarkastellaan liikenneyritysten, matkustajien ja liikennepolitiikan näkökulmasta.  Väitöskirjassa esitetyt julkaisut tarjoavat teoreettisen pohjan, malleja ja empiiriseen aineistoon pohjautuvia näkymyksiä automatisoidun kysyntäohjautuvan joukkoliikenteen ja jaettujen taksipalvelujen politiikkanalyysiin sekä empiirisen tutkimuksen tarpeisiin. Näiden uusien liikennepalveluiden markkinat ovat kehittymässä älyliikennneteknologioiden edistysaskelten ja niitä hyödyntävien innovatiivisten yritysten myötä sekä liikenteen kestävyyteen kohdistuvan kasvavan poliittisen paineen ja vaatimusten myötävaikutuksesta