约束下的按需移动服务建模.pdf

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1、Modeling on-demand mobility services under constraintsChetan Joshi,P.E.(PTV Group)Background Results from testing a framework for modeling on-demand mobilityservices under constraints Motivated by questions and research needs emphasized in Uncertainty in Travel Forecasting:Exploratory Modeling and A

2、nalysis TMIP-EMAT:A Desk Reference,Lemp Et Al.TNCs Today,A Profile of San Francisco Transportation Network Company Activity,Castiglione Et Al.Conference on Innovations in Travel Analysis and PlanningFramework Overview Loosely based on work by Mora*et al.and implemented in PTV-Visum in collaboration

3、with FZI research center,Karlsruhe.Simulates vehicle dispatch operation for on-demand mobility services such as TNCs and pooled ride services.Vehicle insertions from an available fleet controlled via cost function with a penalty for unserved trip requests.Conference on Innovations in Travel Analysis

4、 and Planning(*On-demand high-capacity ride-sharing via dynamic trip-vehicle assignmentJ Alonso-Mora,S Samaranayake,A Wallar,E Frazzoli,D RusProceedings of the National Academy of Sciences 114(3),462-467)Framework Overview Takes trip requests with a pick-up and drop-off location and departure time a

5、nd matches it with available vehicles in the system Allows varying constraints:Vehicle fleet size Vehicle capacity Pooled vs Non-pooled service EV charge points and range Max wait time and detour acceptance Pick-up and drop-off areas Conference on Innovations in Travel Analysis and PlanningApplicati

6、on:Overview Analyze on-demand mobility service scenarios based on realistic data for a major city San Francisco Demand data available from TNCs Today1 Pick-ups and drop-offs TAZ data Network from OpenStreetMap2 DC Fast charger data from DOE3Conference on Innovations in Travel Analysis and Planning1: