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A New Formulation of The Shortest Path Problem with On-Time Arrival Reliability
We study stochastic routing in the PAth-CEntric (PACE) uncertain road ne...
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Eco-Routing Using Open Street Maps
A vehicle's fuel consumption depends on its type, the speed, the conditi...
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Visualization and Travel Time Extraction System for the Statistics of TDCS Travel using MapReduce Framework
Recently, extracting some information as a knowledge from big data is ve...
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Street-level Travel-time Estimation via Aggregated Uber Data
Estimating temporal patterns in travel times along road segments in urba...
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Vehicle Routing and Scheduling for Regular Mobile Healthcare Services
We propose our solution to a particular practical problem in the domain ...
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Optimal dynamic information provision in traffic routing
We consider a two-road dynamic routing game where the state of one of th...
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Deep Learning Based Anticipatory Multi-Objective Eco-Routing Strategies for Connected and Automated Vehicles
This study exploits the advancements in information and communication te...
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Eco-Routing based on a Data Driven Fuel Consumption Model
A nonparametric fuel consumption model is developed and used for eco-routing algorithm development in this paper. Six months of driving information from the city of Ann Arbor is collected from 2,000 vehicles. The road grade information from more than 1,100 km of road network is modeled and the software Autonomie is used to calculate fuel consumption for all trips on the road network. Four different routing strategies including shortest distance, shortest time, eco-routing, and travel-time-constrained eco-routing are compared. The results show that eco-routing can reduce fuel consumption, but may increase travel time. A travel-time-constrained eco-routing algorithm is developed to keep most the fuel saving benefit while incurring very little increase in travel time.
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