CityLines: Hybrid Hub-and-Spoke System for Urban Transportation - - PowerPoint PPT Presentation

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

Private Transit Public Transits

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 Smart Transit

Today’s Transits

• Private Transits

– High Cost – Service delay

• Public Transits

– Fixed routes – Fixed timetable – Long travel time

• No Inter-Transit

Coordination

• Dynamic services

– Real time trip demands

• Short travel time

– as private transits

• Low cost

– as public transits

Future Urban Transit Services

Private Transits: Point-to-point mode Public Transits: fixed route mode

• Hub-and-Spoke Transit Mode

Airlines routes

• 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?

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 D

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S1 D1 S2 S3 D2 D3 D4 S1 D1 S2 S3 D2 D3 D4 Private transit Point-to-point model CityLines 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

S1 D1 S2 S3 D2 D3 D4 S1 D1 S2 S3 D2 D3 D4 Public transit Fixed-route model CityLines 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=0.01o
• 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

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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.

S1 D1 S2 S3 D2 D3 D4

D

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S1 D1 S2 S3 D2 D3 D4

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
• p-Hub problem with L hubs and 1 virtual hub

LP relaxation based approximation solution [2] D1 S1 D1 S2 S3 D2 D3 D4 S1 S2 S3 D2 D3 D4

[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

42 mins Average Travel Time: ~42mins reduction over public transits Slightly higher (4 mins) than private transits Aggregation level: Slightly less (8) than public transits ~23 more over private transits 23 per segment

Average travel time (min) overall trip demands Aggregation level: Average # passengers per trip segment

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

• 80%

12% 35% 25%

Average travel time: 12%–80% reduction rate Average Waiting Time: 25%-35% increase

Comparison with baselines

• Case Studies: Point-to-point Model

• Case Studies: Hub-and-spoke

• Case Studies: Hybrid Hub-and-Spoke

• CityLines Transit System

Cyber-Analytics subsystem Physical-Sensing subsystem Transit-Control subsystem Private Transits Public Transits CityLines Scalability Dynamicity Reliability Compatibility GPS Sets: Trajectories of Vehicles, Trains, …; Automated Fare Collection system: Transactions of all transits; Road Infrastructure: Traffic status from loop detectors, cameras; Trip Demand Prediction Trip QoE Modeling Incentive Mechanism Analysis

• 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?

Questions?