A Hierarchical Approach to Integrated Transit Derek Edwards Georgia Institute of Technology Co-Authors: Aarjav Trivedi, Arun Kumar Elangovan, and Steve Dickerson IEEE Intelligent Transportation Systems Conference: October 6, 2011

Why is Atlantas mass transportation not as efficient and widely used as those in New York City and Washington DC? Crowded Manhattan and Washington Transit Stations Subway Station 1 Empty Midtown Atlanta Bus Stop 1 2

New York, NY Washington, DC Atlanta, GA Population Density (people/mi 2 ) 27,5329,8004,018 Average Weekday Unlinked Transit Trips 10,303,0951,460,125504,420 Typical Headway Between Buses 5-15 minutes8-20 minutes20-45 minutes U.S. Census Bureau, U.S. Census Bureau, County and City Data Book: U.S. Census Bureau, Annual Estimates of the Resident Population for Incorporated Places of 100,000: Rogoff, P.M. Transit Profiles: The Top 50 Agencies national transit database 2009 report year: Metropolitan Transportation Authority, MTA System Schedules, March Metropolitan Atlanta Rapid Transit Authority, Bus Routes and Schedules, March WMATA.com Bus Routes and Scheduled,

Enabling Technologies: Ubiquitous mobile networks, smart phones, GPS. Remove inefficiencies from transportation Optimize bus routes in real time. Automate the car-pooling process Leverage existing infrastructure 4

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The dial-a-ride problem (DARP), is the problem of creating M dynamic vehicle routes to optimally service a set of N passengers curb-to-curb with a priori information of the passengers origins and destinations. CORDEAU, J.-F. and LAPORTE, G., The dial-a-ride problem: models and algorithms, Annals of Operations Research, vol. 153, no. 1, pp. 29–46,

What is the best way for a salesman to visit N cities or locations? For N passengers there are N! permutations. NP-Hard Solved heuristically for large numbers of cities. 7

For N passengers there are N! permutations. NP-Hard Solved heuristically for large numbers of cities. Solution found using Ant Colony Optimization: Distance 14km Travel Time 31:27 What is the best way for a salesman to visit N cities or locations? 8

What is the best way for one or more vehicles to service N pickup and delivery requests? 9

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High Speed Data Trunk Local Data Connection Router/Gateway Local Data Subnet On-Demand Transportation Subnet Transit Station Intra-City Transit High Speed Commuter Rail 12

Provides solution to the last mile problem. Outperforms static transit options in low density areas. Breaks up large DAR network into many small semi-independent networks. 13

Subnets Static Transit System Metro-Wide Transit System A B C A B C 14

Global Objective Function: Operators Objective Function: Passengers Objective Function: 15

Street Network: Node 2 is a Transit Station. EDWARDS, D., et. al.,The Network-Inspired Transportation System: A Hierarchical Approach to Bi-Modal Transit, 14 th International IEEE Conference on Intelligent Transportation Systems, October, Route of Static Bus. On-demand transit out performs static transit for solving the last mile problem. 16

EDWARDS, D., et. al.,The Network-Inspired Transportation System: A Hierarchical Approach to Bi-Modal Transit, 14 th International IEEE Conference on Intelligent Transportation Systems, October, Route of Static Bus. 17

EDWARDS, D., et. al.,The Network-Inspired Transportation System: A Hierarchical Approach to Bi-Modal Transit, 14 th International IEEE Conference on Intelligent Transportation Systems, October, Route of Static Bus. Results: Objective: Minimize VMT Objective: Minimize Passenger Wait and Ride Time 18

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Subnets – The on-demand regions where entire passenger trips can be served by a single vehicle. Size, Shape, Allocation (geographic versus functional) 20

The NITS should accommodate the ride-share option. The ride-share option introduces semi-static routes. A driver with a car has a known origin and destination, but is willing to alter his trip to accommodate others. How should these trips be integrated with static transit? 21

22 Derek Edwards School of Electrical and Computer Engineering Georgia Institute of Technology Steve Dickerson School of Mechanical Engineering Georgia Institute of Technology Arun Kumar Elangovan RideCell, LLC Aarjav Trivedi RideCell, LLC

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1.Encode neighborhood as a graph. Using distances between intersections as weights. 2.Preprocessing: Using Dijkstras Algorithm, create a complete distance graph of the neighborhood. 24

3.Identify location of passengers and destinations of passengers. 4.Use a Genetic Algorithm to determine the optimal order in which to visit the passengers. 25

p 1,j the wait time for passenger j p 2,j the ride time for passenger j p 3,j the total trip time for passenger j 26

Total Vehicle Mile Traveled: Minimize Wait (Green)Minimize Ride (Blue)Minimize Total 27

Total Vehicle Mile Traveled: 4.25 Minimize Wait (Green)Minimize Ride (Blue)Minimize Total 28

Total Vehicle Mile Traveled: 5.55 Minimize Wait (Green)Minimize Ride (Blue)Minimize Total 29