App-based ride-hailing services, such as Uber and Lyft, have become popular thanks to technology advancements including smartphones and 4G/5G network. However, little is known about to what degree their operations impact urban traffic since Transportation Network Companies seldom share their ride data due to business and user privacy reasons. Recently, transportation engineering researchers began to collect data in large cities trying to understand the transportation impacts of ride-hailing services, so as to assist transport planning and policy making. However, (1) there does not exist a general data collection approach applicable to any city, and (2) the studies were based on historical data and cannot project the future easily even though ride-hailing services are developing quickly.

Micromobility refers to small, lightweight vehicles such as shared bicycles and electric scooters (e-scooters). Recently, shared micromobility services see increasing deployment in urban areas to solve the "last mile´ problem, where the travel distance is considered long when walking on foot, but not worth driving a car (e.g., to avoid parking). A key question to ask when deciding whether to deploy a shared micromobility service in an area is: how much car traffic can be reduced during peak hours if this service is deployed? This work answers this question by agent-based transportation simulation. The key challenge here is to generate a realistic synthetic population of the target area along with their travel day-plans. We propose to use an area-specific travel survey plus openly available data sources for this purpose, and demonstrate our approach through a case study that studied the traffic impacts of deploying dockless e-scooters in Birmingham, AL. A demo of our simulation is available at https://youtu.be/zh_mHQ6ck4U.

Micromobility refers to small, lightweight vehicles such as shared bicycles and electric scooters (e-scooters). Recently, shared micromobility services see increasing deployment in urban areas, especially for trips where the travel distance is considered long for walking, but not worth driving a car (e.g., to avoid parking). A key question to ask when deciding whether to deploy a shared micromobility service in an area is: how much car traffic can be reduced during peak hours if this service is deployed? This work answers this question by agent-based transportation simulation. The key contribution is to generate a realistic synthetic population of transportation users in the target area along with their travel day-plans, using an area-specific travel survey plus openly available data sources. We demonstrate our approach through a case study on the deployment of dockless e-scooters in Birmingham, AL, with a demo at https://youtu.be/zh_mHQ6ck4U.

MATSim is the state-of-the-art open source software for agent-based transport simulation, intended for use to evaluate transportation planning models. A standard approach to use MATSim is to conduct a user survey about their day-plans of travel, from which a synthetic dataset of agents' day-plans for an entire region is generated for transport simulation. The simulation output can be used for various evaluations, such as congestion conditions of road segments and their peak hours.This paper aims to conduct a transportation simulation on MATSim for the region of Birmingham, AL. A traditional approach based on Iterative Proportional Fitting (IPF) is not sufficient for generating a realistic synthetic population due to the small data problem: Birmingham is a small city with limited transport data statistics, and we only have a survey of 451 people for their day-plans. To tackle the small data problem, we seek the assistance of abundant open data such as US Census data, OpenStreetMap, OpenAddresses and Birmingham Business}{Alliance to complete the fine details realistically. We also utilize various data science and machine learning techniques to build models that utilize these open data to generate a realistic population. Preliminary tests demonstrate reasonable accuracy of the simulation results.

This poster aims to conduct a transportation simulation on MATSim, the state-of-the-art open source software for agent-based transportation simulation, for the region of Birmingham, AL, where a synthetic population is generated from a survey of 451 people with their day-plans of traveling. To tackle the small data problem, we seek the assistance of abundant open data such as US Census data, OpenStreetMap, OpenAddresses and Birmingham Business Alliance to complete the fine details realistically. We also utilize data science and machine learning techniques as well as iterative proportional fitting to build models that utilize these open data to generate a realistic population. Good accuracy of the simulation is achieved; see https://youtu.be/ZIm0WsmKB4E for a demo.