ride-sourcing (tnc service) and transit in shanghai
TRANSCRIPT
Friend or Foe? App-based, on-demand ride services (ride-sourcing) and transit in Shanghai
UC Berkeley Ruoying Xu PhD student at the Department of City and Regional planning Yiyan Ge Concurrent Masters student at DCRP and Transportation Engineering
Friend or Foe? App-based, on-demand ride services (ride-sourcing) and transit in Shanghai Transportation Planning & Urban Data Science
UC Berkeley Ruoying Xu PhD student at the Department of City and Regional planning Yiyan Ge Concurrent Masters student at DCRP and Transportation Engineering
What question are we trying to answer and why? How do we approach the question? How do we implement the approach? What can we do with the findings?
OUTLINE
We make choices • Demand • Travel mode • Travel time • Location
preferences
Choices have consequences • Traffic • GHG emission • Land use patterns
TNC
Equity and Access
Is ride-sourcing (TNC) competing with transit in cities?
Traditional approach: Whether people actually switched from a transit mode that they were previously using to the new mode TNC for the same trip purpose?
Technology-enabled, data-rich, fast-paced changes
Quick understanding of changes & responsive and responsible policies
Quick understanding of changes & responsive and responsible policies
Technology-enabled, data-rich, fast-paced changes Analytical + Confirmatory approach
Broad patterns and correlations
Theories & known underlying mechanisms
Analytical + Confirmatory approach
DATA
Trip data from Jan. to Oct., 2015, provided by Didi Kuaidi Trip origin and destination Trip date and time 140,854 samples in total
January as the base year: 6098 trips Total sample size: 140,854 trips
Total TNC trip changes over 10 months
Assumption 1
When TNC trip price decreases, people take more TNC trips, including trips with transit alternatives.
TNC
TRANSIT INDUCED DEMAND
If there is a reasonable transit alternative available for the TNC trip OD [Competition?]
No reasonable transit alternative available
Assumption 2 • Low car ownership
(~15%) • High transit usage
(50% of trips) • Limited taxi supply
(20 per 10000 ppl)
Assumption 2 Origin + destination + day of week + time of day + transit mode à Google Map Direction API à transit alternative Reasonable transit alternative:
• Waiting time < 20 min • Walking time < 30 min • Number of transfer at most 1 • Transit travel time / TNC travel time
ratio <= 2
Is ride-sourcing (TNC) competing with transit in cities?
Individual level: Assumptions on travel behaviors
Is ride-sourcing (TNC) competing with transit in cities?
Individual level: Assumptions on travel behaviors
Hypothesis: e.g. people are more likely to use TNC service for short-distant trips
Is ride-sourcing (TNC) competing with transit in cities?
Individual level: Assumptions on travel behaviors
Hypothesis: e.g. people are more likely to use TNC service for short-distant trips
EXPECTED differences and changes in % of TNC trips that can be reasonably replaced by transit
Is ride-sourcing (TNC) competing with transit in cities?
Individual level: Assumptions on travel behaviors
Hypothesis: e.g. people are more likely to use TNC service for short-distant trips
EXPECTED differences and changes in % of TNC trips that can be reasonably replaced by transit
OBSERVED differences and changes in % of TNC trips that can be reasonably replaced by transit
Key Questions
In what circumstance, ride-sourcing service is more competitive with transit? When: 1. the trip distance is short? 2. the transit alternative is bus-only? 3. the trip takes place during peak-hour?
% of TNC trips with metro-only or bus-only alternatives that can be reasonably replaced by metro or bus
Takeaway
Ride-sourcing is more likely to be competing with transit: 1. when it is a long trip 2. when the transit alternative is metro
Prices affect different types of trips differently There is strong indication of induced demand
Transportation Planning
Transportation planning policies that are grounded in neither theories nor evidence
Lagging transportation planning policies that respond to the past
Transportation planning policies that are grounded in neither theories nor evidence
Lagging transportation planning policies that respond to the past
No RIGHT process Correlation is fine too Collaborations between data owners and planning agencies Responsible and responsive transportation planning policies
Transportation Planning
Urban Data Science
Thank you. Contacts: Ruoying Xu: [email protected] Yiyan Ge: [email protected]