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Case Study

MTA: New York

Developing a sustainable and scalable on-demand transit proposal.

MTA needed to provide a better transport solution to the people of New York City’s outer areas. Discover what they learned when they engaged Liftango.

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Overview

Liftango was engaged by the MTA for a simulation service to predict the uptake
for an implemented on-demand service. Liftango’s simulation technology was provided to MTA as a benchmark to measure the realism and efficiency of tender proposals from shared mobility providers. Essentially, enabling MTA to make an educated decision on whom they should choose as their on-demand provider.

The Metropolitan Transportation Authority is North America’s largest transportation network, serving a population of 15.3 million people across a 5,000-square-mile travel area surrounding New York City through Long Island, southeastern New York State, and Connecticut.

The MTA network comprises the nation’s largest bus fleet and more subway and commuter rail cars than all other U.S. transit systems combined. The MTA’s operating agencies are MTA New York City Transit, MTA Bus, Long Island Rail Road, Metro-North Railroad, and MTA Bridges and Tunnels.

The Problem

MTA needed to provide a better transport solution to the people of New York City’s outer areas. Why? Existing bus services being less frequent than a subway service or completely unavailable to late-night workers. Late shift workers are increasing in importance. Many of the late shift sectors including healthcare and food services are expected to be one of the largest growing sectors in the next five to ten years. The census shows that a number of people are leaving for work between 3-6 pm and therefore returning during the overnight period.

The MTA needed to have an equitable transport solution for these members of the community that was efficient, affordable and safe.

As MTA’s first time launching this type of project, there was some risk surrounding launch. By engaging Liftango, the aim was to mitigate risk, simulate possible outcomes and ensure service feasibility.

The Solution

Data provided by MTA enable feasibility simulations to be conducted by Liftango. This included:

  • Zone health checks
  • Estimate demand
  • Calculating anticipated ridership
  • Test multiple zone options for operation
  • Test the most cohesive service for the current transport network

The Results

Simulation outputs enable a real-world assessment and subsequent data-driven launch decisions.

Liftango devised the best service areas for the project, including analysing current services, to make sure it complemented the current transport network.

Using anticipated ridership data, Liftango deemed this a viable service that would provide the people of New York City with an improved transport network.

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