Simulated Multimodal Transit Trip-Chain Planner Case Study of Arlington, TX, Showed Reducing Weekly and Monthly Ride Pass Prices Boosted Total Daily Revenue by $102 and Increased Total Rider Economic Benefit (Consumer Surplus) by $363 Per Day.
Case Study of a Microtransit Service in Texas Using Trip Planner Behavioral Modeling with Synthetic and Real World Data.
Arlington, Texas, United States
Multimodal Trip-Chain Planner for Incentivizing Transit Usage
Summary Information
Microtransit is a shared demand-responsive transportation service that typically provides first- and last-mile trips or operates over larger areas in locations without other transit options. Diverse travel patterns and user preference combined with limited ground-truth data can make it challenging to estimate system demand and revenue.
This study proposed a behavioral model that examined how pricing strategies could increase microtransit use and revenue, and applied it to a case study in Arlington, Texas. The study also developed a multimodal trip-chain planner that optimized multiple trips, integrated various transportation modes, and accommodated travel preferences.
METHODOLOGY
This study used synthetic trip data and microtransit usage data as inputs for an agent-based behavioral model for microtransit. The synthetic trip dataset comprised 1.3 million trips (origin and destination, mode, distance, duration, and cost) made by 400 thousand Arlington residents on a typical weekday and weekend in 2023. The microtransit usage data (daily ridership, number of subscribers, average in-vehicle time, average utilization rate, and pickup/drop-off heatmaps) were from the City of Arlington’s existing service, which operated citywide since 2021.
The study modeled two policy scenarios to evaluate ridership, ride pass subscribers, total revenue, and total consumer surplus: (1) A fixed-price ride pass, and (2) discounted or free rides for specific events or destinations. The specific event was a trip serving the football stadium of the Dallas Cowboys. The specific destination was a trip serving Medical City Arlington, a healthcare facility.
Findings reflect modeled scenario outcomes using synthetic and observed operational inputs; they are not measured impacts from an implemented fare policy.
FINDINGS
Scenario 1:
- Decreasing the cost of a monthly ride pass from $80 to $71.50 maximized total revenue resulted in a $102 increase in total daily revenue.
- Decreasing the cost of a monthly ride pass from $80 to $49.40 and a weekly pass from $25 to $13.10 maximized both revenue and consumer surplus. It resulted in a daily consumer surplus of $363.
Scenario 2:
- A 100 percent fare discount on stadium trips reduced driving from 61.3 to 59.2 percent (approximately 80 car trips per game day) but required a subsidy of $617 per day (annual subsidy of $32,068).
- A 100 percent discount on Medical City trips increased microtransit ridership from 42 to 140 trips per day. It required a subsidy of $544 per day (annual subsidy of $141,440 per year).
