Principal Investigator

Benjamin Coifman

Status

Completed

Start Date

Nov. 30, 2016

End Date

July 20, 2020

Project Type

Research Applied

Grant Program

FAST Act - Mobility National (2016 - 2022)

Grant Cycle

Mobility21 - The Ohio State University

Visibility

Public

Abstract

Project Lead: Prof. Mark McCord

This research seeks to develop a viable, on-going monitoring program that utilizes municipal vehicles as sensor platforms and uses these sensors to monitor the roadway traffic conditions and performance of signalized intersections in a metropolitan area. The results are aimed to prioritize the roadways and signals in greatest need of improvements and retiming. The envisioned system would piggyback sensors on municipal vehicles that are already traveling the network in the course of their normal duties. This opportunistic data collection approach will collect "snapshots" of roadway segments and signalized intersection approaches separately from other sensors that may be available whenever one of these host vehicles happens to pass through. Municipal vehicles – e.g., city buses, police cars, city maintenance vehicles – eventually cover the entire road network in their normal duties, thereby eliminating most of the dedicated labor to collect the traffic data. The ultimate goal is to make better-informed resource allocation to determine when and where to deploy conventional traffic studies and advanced signal controls – i.e., to develop a new complementary tool to improve the effectiveness and efficiency of the existing tools.

Year 1 is intended to develop, demonstrate and validate the methodology using an instrumented probe vehicle and 3-6 hours of data per week collected over a period of months. We will demonstrate the ability to assess the roadway traffic conditions and corresponding performance of individual traffic signals using data from an instrumented probe vehicle. For independent ground truth, we will aim to coordinate with local operating agencies to ensure that our data collection overlaps with their conventional traffic-studies by including corridors slated for conventional timing studies in our tours.

In year 2 we will validate the quality of the proposed roadway traffic condition and intersection control assessment approach. The collected data will be archived, analyzed, and used to improve the monitoring algorithms. We will also develop a plan to deploy the system on a municipal vehicle. The deployment plan will most likely start with considering OSU’s Campus Bus Service (CABS), which operates a fleet of almost forty 40-foot buses on several routes that travel both on and off campus serving area of varied land-uses.    

Description

This research seeks to develop a viable, on-going monitoring program that utilizes municipal vehicles as sensor platforms and uses these sensors to monitor the roadway traffic conditions and performance of signalized intersections in a metropolitan area. The results are aimed to prioritize the roadways and signals in greatest need of improvements and retiming. The envisioned system would piggyback sensors on municipal vehicles that are already traveling the network in the course of their normal duties. This opportunistic data collection approach will collect "snapshots" of roadway segments and signalized intersection approaches separately from other sensors that may be available whenever one of these host vehicles happens to pass through. Municipal vehicles – e.g., city buses, police cars, city maintenance vehicles – eventually cover the entire road network in their normal duties, thereby eliminating most of the dedicated labor to collect the traffic data. The ultimate goal is to make better-informed resource allocation to determine when and where to deploy conventional traffic studies and advanced signal controls – i.e., to develop a new complementary tool to improve the effectiveness and efficiency of the existing tools.

Timeline

CMU Subcontract with OSU Signed: 6/27/2017

Year 1 is intended to develop, demonstrate and validate the methodology using an instrumented probe vehicle and 3-6 hours of data per week collected over a period of months. We will demonstrate the ability to assess the roadway traffic conditions and corresponding performance of individual traffic signals using data from an instrumented probe vehicle. For independent ground truth, we will aim to coordinate with local operating agencies to ensure that our data collection overlaps with their conventional traffic-studies by including corridors slated for conventional timing studies in our tours.

In year 2 we will validate the quality of the proposed roadway traffic condition and intersection control assessment approach. The collected data will be archived, analyzed, and used to improve the monitoring algorithms. We will also develop a plan to deploy the system on a municipal vehicle. The deployment plan will most likely start with considering OSU’s Campus Bus Service (CABS), which operates a fleet of almost forty 40-foot buses on several routes that travel both on and off campus serving area of varied land-uses.

Strategic Description / RD&T

Deployment Plan

Expected Outcomes/Impacts

Expected Outputs

TRID

Individuals Involved

Email	Name	Affiliation	Role	Position
coifman.1@osu.edu	Coifman, Benjamin	The Ohio State University	PI	Faculty - Tenured
hillstrom.7@osu.edu	Hillstrom, Stacy	The Ohio State University	Other	Other
mccord.2@osu.edu	McCord, Mark	The Ohio State University	Co-PI	Faculty - Tenured
mishalani.1@osu.edu	Mishalani, Rabi	The Ohio State University	Co-PI	Faculty - Tenured
ozguner.1@osu.edu	Ozguner, Umit	The Ohio State University	Co-PI	Faculty - Tenured
redmill.1@osu.edu	Redmill, Keith	The Ohio State University	Co-PI	Other

Budget

Amount of UTC Funds Awarded

$191287.00

Total Project Budget (from all funding sources)

$438070.00

Documents

Type	Name	Uploaded
Progress Report	76_Progress_Report_2018-03-30	March 29, 2018, 9:22 a.m.
Progress Report	76_Progress_Report_2018-09-30	Sept. 30, 2018, 9:47 a.m.
Project Brief	McCord_et_al_2019_slides.pptx	April 30, 2019, 9:30 a.m.
Project Brief	McCord_et_al_2019_description.docx	April 30, 2019, 9:30 a.m.
Data Management Plan	dmp-McCord-2019.docx	April 30, 2019, 9:32 a.m.
Progress Report	76_Progress_Report_2019-09-30	Sept. 29, 2019, 2:09 p.m.
Presentation	OSU_Sensing_Platforms_Project_Presentation_CMU_webinar_2020-02-04.pdf	March 27, 2020, 1:47 p.m.
Progress Report	76_Progress_Report_2020-03-31	March 27, 2020, 1:47 p.m.
Progress Report	76_Progress_Report_2020-09-30	Sept. 25, 2020, 10:47 a.m.
Final Report	Final_Report_-_76_2020.pdf	Nov. 23, 2020, 6:15 a.m.
Publication	A framework for estimating long term driver behavior	Dec. 27, 2020, 10:20 p.m.
Publication	Velocity profile optimization of on road vehicles: Pontryagin's Maximum Principle based approach	Dec. 27, 2020, 10:21 p.m.
Publication	A model predictive-based approach for longitudinal control in autonomous driving with lateral interruptions	Dec. 27, 2020, 10:24 p.m.
Publication	Vehicle speed prediction using a cooperative method of fuzzy Markov model and auto-regressive model	Dec. 27, 2020, 10:24 p.m.
Publication	Situational assessments based on uncertainty-risk awareness in complex traffic scenarios	Dec. 27, 2020, 10:25 p.m.
Publication	Distributed model predictive control of spatially interconnected systems using switched cost functions	Dec. 27, 2020, 10:26 p.m.
Publication	Automated rotational calibration of multiple 3D LIDAR units for intelligent vehicles	Dec. 27, 2020, 10:27 p.m.
Publication	Dynamic eco-driving’s fuel saving potential in traffic: Multi-vehicle simulation study comparing three representative methods	Dec. 27, 2020, 10:27 p.m.
Publication	Dynamic Probabilistic Risk Assessment of Unmanned Aircraft Adaptive Flight Control Systems	Dec. 27, 2020, 10:28 p.m.
Publication	Unifying analytical methods with numerical methods for traffic system modeling and control	Dec. 27, 2020, 10:31 p.m.
Publication	Distributed model predictive control for cooperative and flexible vehicle platooning	Dec. 27, 2020, 10:32 p.m.
Publication	Synthesis of a behavior-guided controller for lead vehicles in automated vehicle convoys	Dec. 27, 2020, 10:33 p.m.
Publication	Dynamic modeling and control of high-speed automated vehicles for lane change maneuver	Dec. 27, 2020, 10:34 p.m.
Publication	Social force based microscopic modeling of vehicle-crowd interaction	Dec. 27, 2020, 10:34 p.m.
Publication	Real-time traffic scene segmentation based on multi-feature map and deep learning	Dec. 27, 2020, 10:38 p.m.
Publication	Driver Models for Both Human and Autonomous Vehicles	Dec. 27, 2020, 10:39 p.m.
Publication	Driver Models for Both Human and Autonomous Vehicles with Different Sensing Technologies and Near-Crash Activity	Dec. 27, 2020, 10:39 p.m.
Publication	Pre-Crash Interactions between Pedestrians and Cyclists and Intelligent Vehicles	Dec. 27, 2020, 10:40 p.m.
Publication	Technology and Enhancements to Improve Pre-Crash Safety	Dec. 27, 2020, 10:41 p.m.
Publication	Model predictive trajectory optimization and tracking for on-road autonomous vehicles	Dec. 27, 2020, 10:42 p.m.
Publication	Top-view trajectories: A pedestrian dataset of vehicle-crowd interaction from controlled experiments and crowded campus	Dec. 27, 2020, 10:43 p.m.
Publication	Driving Intention Recognition and Lane Change Prediction on the Highway	Dec. 27, 2020, 10:44 p.m.
Publication	A Framework for Automated Collaborative Fault Detection in Large-Scale Vehicle Networks	Dec. 27, 2020, 10:45 p.m.
Publication	Control Method Designs and Comparisons for Tractor-Trailer Vehicle Backward Path Tracking	Dec. 27, 2020, 10:46 p.m.
Publication	Combining Social Force Model with Model Predictive Control for Vehicle's Longitudinal Speed Regulation in Pedestrian-Dense Scenarios	Dec. 27, 2020, 10:47 p.m.
Publication	An Online Evolving Framework for Modeling the Safe Autonomous Vehicle Control System via Online Recognition of Latent Risks	Dec. 27, 2020, 10:48 p.m.
Publication	Personalized Safety-focused Control by Minimizing Subjective Risk	Dec. 27, 2020, 10:49 p.m.
Publication	Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving	Dec. 27, 2020, 10:49 p.m.
Publication	A Social Force Based Pedestrian Motion Model Considering Multi-Pedestrian Interaction with a Vehicle	Dec. 27, 2020, 10:50 p.m.
Publication	Fuel-Optimal Centralized Control of a Cooperating Car Swarm	Dec. 27, 2020, 10:51 p.m.
Publication	Optical Flow based Visual Potential Field for Autonomous Driving	Dec. 27, 2020, 10:51 p.m.
Publication	An online evolving framework for advancing reinforcement-learning based automated vehicle control	Dec. 27, 2020, 10:53 p.m.
Publication	A Modeled Approach for Online Adversarial Test of Operational Vehicle Safety	Dec. 27, 2020, 10:54 p.m.
Publication	Roadway Traffic Data Collection from Mobile Platforms	March 21, 2021, 4:55 p.m.
Publication	Municipal Vehicles as Sensor Platforms to Monitor Roadway Traffic	March 21, 2021, 4:56 p.m.
Publication	Image-based vehicle tracking from roadside LiDAR data	May 3, 2022, 5:11 p.m.
Publication	Traffic flow theory and characteristics	May 3, 2022, 5:12 p.m.
Publication	Quantifying what goes unseen in instrumented and autonomous vehicle perception sensor data–A case study	May 3, 2022, 5:12 p.m.
Publication	Vehicle tracking and speed estimation from roadside lidar	May 3, 2022, 5:13 p.m.

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Partners

Name	Type
The Ohio State University	Deployment Partner Deployment Partner
Mid-Ohio Regional Planning Commission	Deployment Partner Deployment Partner