# Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs

## TL;DR

This paper introduces a mobile sensor-based framework for real-time pedestrian arrival rate estimation in MOD networks, enhancing traffic data collection without fixed infrastructure.

## Contribution

It proposes a novel distributed fusion algorithm and a moving observer method for accurate pedestrian arrival rate estimation using onboard sensors.

## Key findings

- Achieved 90% pedestrian detection hit rate with low false positives.
- Demonstrated comparable accuracy to stationary sensors in simulations and hardware tests.
- Enhanced real-time traffic data collection for MOD systems without fixed sensors.

## Abstract

Mobility On Demand (MOD) systems are revolutionizing transportation in urban settings by improving vehicle utilization and reducing parking congestion. A key factor in the success of an MOD system is the ability to measure and respond to real-time customer arrival data. Real time traffic arrival rate data is traditionally difficult to obtain due to the need to install fixed sensors throughout the MOD network. This paper presents a framework for measuring pedestrian traffic arrival rates using sensors onboard the vehicles that make up the MOD fleet. A novel distributed fusion algorithm is presented which combines onboard LIDAR and camera sensor measurements to detect trajectories of pedestrians with a 90% detection hit rate with 1.5 false positives per minute. A novel moving observer method is introduced to estimate pedestrian arrival rates from pedestrian trajectories collected from mobile sensors. The moving observer method is evaluated in both simulation and hardware and is shown to achieve arrival rate estimates comparable to those that would be obtained with multiple stationary sensors.