Using Farecard Data to Suggest Cycling Policies in Singapore. Ashwani Kumar Viet Anh Nguyen Kwong Meng Teo Amedeo Odoni

Transcription

1 Using Farecard Data to Suggest Cycling Policies in Singapore Ashwani Kumar Viet Anh Nguyen Kwong Meng Teo Amedeo Odoni

2 Based on our paper published in the journal Annals of Operations Research in April 2014.

3 Why Cycling? Clean, cheap and non-congesting private mode of transport Competitive travel times (vis-à-vis car) in city environment for short-distance trips Health benefits

4 Types of Cycling

5 Our Focus: Commuter Cycling Because Commuting trips have a big share of total trips Commuting affects peak-hour traffic congestion Predictable flows: easier to plan Governments more concerned about it : economic and environmental implications

6 Research Gaps Lack of easy, reliable methods to estimate potential demand for commuter cycling Need for more objectivity in cycling related policymaking

7 Contributions A fare-card (EZ-link) data based methodology to analyze commuter cycling demand, its application to Singapore Policy suggestions An optimization model to facilitate the policy decisions

8 Singapore: Key Related Facts Low ( 1%) modal share of commuter cycling in Singapore Recent policy interventions to promote cycling Cycling towns Cycle parking at MRT stations Park connectors A popular and expanding MRT network Loss-making feeder bus services vs Profitable long distance bus services

9 Singapore MRT Map

10 Methodology to Estimate Demand for Commuter Cycling Trip distance is a KEY criterion for the likely switch of a commuting trip from other modes to cycle, assuming adequate cycling infrastructure and facilities are provided. Potential commuter cycling trips could be: first-mile trips to MRT stations end-to-end short-distance trips We use EZ-link data to find the number of these trips during morning hours(6.30 AM 9 AM) We take: 3km maximum distance for the first-mile cycling to MRT stations 5km maximum distance for the end-to-end cycling.

11 Data Analysis: Key Facts & Observations About 9 million records for a working week analyzed 25% of all public transport commutes are short- distance(<5km) 12% of all public transport commutes involve short- distance (<3km) first-mile (home-end) trip to MRT stations Just 19 out of 89 MRT stations cover more than 70% of the first-mile trips Compared to first-mile trips, end-to-end short-distance public transport trips are geographically dispersed Significant number of end-to-end short distance trips are by students; concentrated in small regions

12 Number of First-mile Trips Number of End-to-end Trips Distance Distribution of Short-distance Public Transport Trips (6.30AM-9AM) Adult Students 0 0 to 1 1 to 2 2 to 3 3 to 4 4 to 5 Trip Distance (km) 0 0 to 1 1 to 2 2 to 3 3 to 4 4 to 5 Trip Distance (km)

13 Spatial Distribution of First-mile (<3KM) Trips

14 Short-distance (<5km) End-to-end trips

15 Suggested Policies Cycling towns: cycling infrastructure and facilities centered around MRT stations Cycling links: cycling tracks/lanes connecting cycling towns Cycling regions: by combining together towns and links School cycling enclaves: specific cycling infrastructure in neighborhood of schools with heavy short-distance end-toend flows

16 Cycling Regions: First-mile Plus Endto-end Flows

17 West Cycling Region s Flows

18 Decision Support Optimization Model Objective: maximize number of commuter cyclists, subject to Constraints Origin and destination of cycling links must be cycling towns Budget constraint Binary nature of decision variables Parameters: cost of a cycling town, unit cost of a cycling link, Percentage modal switch to cycling

19 Why need an Optimization Model? While choice of cycling towns apparently looks straightforward with policy makers picking MRT stations in decreasing order of first mile demand, the integration within cycling regions introduces a higher level of complexity. On one hand, this complexity arises from a large number of end-to-end demands which are sparsely distributed over the island. On the other hand, each cycling link can serve multiple end-to-end demands at the same time. For example, considering three cycling towns in a straight line A-B-C : link A-B serves not only demand from A to B and vice versa, but also demand between A and C. The requirement of cycling towns on both ends of a chosen cycling link adds to complexity

20 Optimization Model Details

21 Optimization Model Details

22 Net Benefits Maximization Model

23 Improvisation Subject to: Constraints (1.1) to (1.10) plus budget constraint

24 Solution of the Model Budget = S$100 million Cost of a CT =S$10 million Cost of CL = S$0.5 million per km 10% switch to cycle

25 Future Work Understanding other factors affecting commuter cycling demand to have better demand numbers Including uncertainty in the decision support model