  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.
