CityLines: Hybrid Hub-and-Spoke System for Urban Transportation Services Yanhua Li Assistant Professor Computer Science Department Worcester Polytechnic Institute
� Global Urbanization and Transportation
� Today’s Urban Transit Services Public Transits Private Transit affordable ride-sharing services reduce the personal vehicle usage
� Limitations of Today’s Public Transits • Fixed Routes and Time Tables – Transit supply mis-match dynamic demands • Large number of stops and transfers – Long travel time
� Limitations of Today’s Private Transits • Expensive – High operation cost, – Due to the exclusive service • Service delay – On-demand services – Delay after the service request • Transit modes run independently – Lack of inter-transit coordination
� Future Urban Transit Services Today’s Transits Future Smart Transit • Private Transits • Dynamic services – High Cost – Real time trip demands – Service delay • Short travel time • Public Transits – as private transits – Fixed routes • Low cost – Fixed timetable – as public transits – Long travel time • No Inter-Transit Coordination Private Transits: Point-to-point mode Public Transits: fixed route mode
� Hub-and-Spoke Transit Mode • Traffic move along spokes connected via a few hubs – Less operation cost (than private), thus lower cost – Less stops/stations (than public), thus lower transit time • A promising transit mode, and how to design it in urban areas? Airlines routes Package delivery system
� CityLines Transit System • CityLines: a Hybrid Hub-and-Spoke Transit Mode – point-to-point mode: high demand source-destination pairs – hub-and-spoke mode: low demand source-destination pairs S 1 S 1 D 2 D 2 D D 1 D 1 D 3 D 3 1 S 2 S 2 D 4 D 4 S 3 S 3 Private transit CityLines Point-to-point model Hybrid hub-and-spoke mode Reduce routes, thus operation cost
� CityLines Transit System • CityLines: a Hybrid Hub-and-Spoke Transit Mode – point-to-point mode: high demand source-destination pairs – hub-and-spoke mode: low demand source-destination pairs S 1 S 1 D 2 D 2 D 1 D 1 D 3 D 3 S 2 S 2 D 4 D 4 S 3 S 3 Public transit CityLines Fixed-route model Hybrid hub-and-spoke mode Reduce stops/stations, thus travel time
�� CityLines Transit System Design
Input Data Description • Trip Demand Data (in Shenzhen): • Source: Taxi GPS, Bus, Subway Transactions • Duration: March 1st–30th, 2014. • Size: 19,428,453 trips in all transit modes • Format: Taxi ID, time, latitude, longitude, load • Road Map, Subway Lines, and Bus routes:
Stage 1: Road Map Gridding • Given a side length s=0.01 o • 1,508 grids are obtained • 1,018 grids are strongly connected by road network
Stage 2: Trip demand aggregation • Trip demand: <src, dst, t> • Aggregated trip demand <src_grid, dst_grid, t> 6am to 9am No demand Low demand Medium demand High demand The spatial distribution of trip demand sources
Stage 3: Optimal Hybrid Hub-and- Spoke Planning • Problem definition: • Given: n spokes, a set of K trip demands, a budget of M point-to-point paths, L Hub stations • How to plan the hybrid hub-and-spoke network? • Goal: Minimize the average travel time • Constraints: Up to one-stop (at a hub) per trip S 1 D 2 D 1 D 3 S 2 D 4 S 3
Stage 3: Optimal Hybrid Hub-and- Spoke Planning • Challenges: • A large number of hub candidates: all spokes • n=1,018 spokes; L=10 hubs; • Joint modeling of point-to-point and hub-and-spoke • Two Components: • Optimal Hub Selection (OHS): Find L+M hub candidates • Goal: “Cover” the most shortest paths of trip demands • Optimal Trip Assignment (OTA): Hub-spoke net with L hubs • Goal: Minimize the average travel time • (introducing virtual hub to model the joint optimization )
Stage 3-I: Optimal Hub Selection (OHS) • Problem Definition: • Find M+L hub candidates • Goal: “Cover” the most trip demands • A hub h covers a trip demand < src, dst, t >, • If h is on the shortest path from src to dst. S 1 S 1 D 2 D 2 D D 1 D 1 D 3 D 3 1 S 2 S 2 D 4 D 4 S 3 S 3 L=2, M=1, L+M=3
Stage 3-I: Optimal Hub Selection (OHS) • Maximum Coverage Problem • NP-Hard Problem • Approximate Algorithm with rate 1-1/e [1] [1] D. S. Hochbaum. Approximating covering and packing problems: set cover, vertex cover, independent set, and related problems. In Approximation algorithms for NP-hard problems. PWS Publishing Co., 1996.
Stage 3-II: Optimal Trip Assignment • p-Hub problem for hub-and-spoke model S 1 S 1 D 2 D 2 D 1 D 1 D 3 D 3 S 2 S 2 D 4 D 4 S 3 S 3 • p-Hub problem with L hubs and 1 virtual hub LP relaxation based approximation solution [2] [2] A. T. Ernst and M. Krishnamoorthy. Exact and heuristic algorithms for the uncapacitated multiple allocation p-hub median problem. European Journal of Operational Research, 1998.
Comparison with �� Public and Private Transits Average travel time (min) overall trip demands Aggregation level: Average # passengers per trip segment 42 mins 23 per segment Aggregation level: Average Travel Time: Slightly less (8) than public ~42mins reduction over public transits transits Slightly higher (4 mins) than private transits ~23 more over private transits
Evaluation • Optimal Hub Selection • Baselines • Rand-Sel: Random hub selection • Top-Sel: Top coverage hub selection • OHS algorithm • Optimal Trip Assignment • Baselines • Rand-Ass: Random Trip assignment • Ave-Ass: Average Trip assignment • OTA algorithm
�� Comparison with baselines 25% 35% 80% 12% Average Waiting Time: Average travel time: 25%-35% increase � 12%–80% reduction rate �
�� Case Studies: Point-to-point Model
�� Case Studies: Hub-and-spoke
�� Case Studies: Hybrid Hub-and-Spoke
�� CityLines Transit System • CityLines: a Hybrid Hub-and-Spoke Transit Mode – point-to-point mode: high demand source-destination pairs – hub-and-spoke mode: low demand source-destination pairs – Autonomous vehicles? Dynamicity Compatibility Scalability Reliability Public Transits Transit-Control Private Transits CityLines subsystem Cyber-Analytics Trip QoE Trip Demand Incentive subsystem Modeling Prediction Mechanism Analysis Physical-Sensing GPS Sets: Trajectories of Vehicles, Trains, … ; subsystem Automated Fare Collection system: Transactions of all transits; Road Infrastructure: Traffic status from loop detectors, cameras;
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