Improved Method for Estimating Vehicle Lengths under Congested Traffic

1. Improved Method for Estimating Vehicle Lengths under Congested Traffic OTEC 2014 By Qingyi Ai ARCADIS US INC., Cleveland OH Oct 2014

2. M M S S Dual-loop detector on freeway Introduction • Dual-loop detector • An inductive-loop station is usually made up of several parts: wire loops, lead-in wires, lead-in cables, a pull box, and a controller • When a vehicle enters or leaves the loop, the electronics unit will send a pulse to the controller. This pulse is recorded and indicates that a vehicle is detected • Information obtained from dual-loop detectors includes timestamp, vehicle count and occupancy. Based on this information, vehicle speed and vehicle length can be calculated. Traffic Flow

3. Introduction • Dual-loop detector data is widely used as a data source for vehicle classification because dual-loop detectors are reliable and less costly. • Length-based vehicle classification plays a very important role in many transportation areas • Traffic operation and management • Transportation planning • Traffic related air quality analysis

4. Distance between two single loops Dual-loop station M M S S t1 t3 t4 t2 time Introduction • The Existing Vehicle Classification model • Assumption: The difference between on times for both loops is very small. Where, D = distance between two single loops in the dual-loop station (ft); t = t3 - t1; and OnT1 = t2 - t1; and OnT2 = t4 - t3.

5. Introduction • Under non-free flow, the difference between on times on two loops is often large. The existing dual-loop length-based vehicle classification model produces many errors under congested traffic conditions, especially under stop-and-go traffic flow. • The errors arecontributed by the complex characteristics of traffic flows under congestion; but quantification of such contributing factors remains unclear. • The algorithm of screening dual-loop detector data may remove those data points which actually are good. • An updated model isneeded.

6. Methodology • Vehicle trajectory data extracted from video acts as the ground-truth data • Use the concurrent event dual-loop data to evaluate vehicle classification models against the ground-truth data • GPS data is employed to reveal traffic characteristics of different traffic states • Develop improved vehicle classification models integrating traffic-related factors

7. Camera Location Dual-loop detectors Data Collection • Study site: I-70/71 Downtown Columbus, OH • Dual-loop stations work properly • Recurrent traffic congestion Study Site

8. Data Collection • Study site: Williams Ave & I-71 Cincinnati, OH Study Site Study Site

9. Data Collection • Video Data Used as Ground-truth Data • 26 hours video data for 3 days at the study sites in Columbus; 8 hours video data for 2 days at the study site in Cincinnati. • Concurrent event dual-loop data obtained from the TMC at ODOT. • GPS data is collected using a probe car equipped with a GPS data logger

10. Data Extraction • Trajectory Data Extraction in VEVID • Software VEVID (Vehicle Video-Capture Data Collector) developed by Dr. Heng Wei, University of Cincinnati • Using VEVID to extract vehicle speeds, timestamps, and lengths.

11. Existing Model Evaluation • Evaluating Existing Vehicle Classification Model • Against ground-truth under free flow traffic • T-test is employed and the output from the existing model under free flow traffic is acceptable.

12. Existing Model Evaluation • Evaluating Existing Vehicle Classification Model • Under congested traffic, the existing model produced many errors, especially for large vehicles. • Updated or new models are needed to estimate vehicle lengths under congested traffic conditions.

13. M S M S Scenario 5 Scenario 1 M S M S Scenario 6 Scenario 2 M S M S Scenario 7 Scenario 3 M S M S Scenario 8 Scenario 4 Vehicles operating status • Vehicles Possible Operating Status over the Detection Area under Congested Traffic • Assumptions: 8 scenarios

14. Vehicles operating status • Statistical Analysis of Operating Status

15. Congested Traffic yes Scenario 1 OnT1<ts1, and OnT2<ts1 OnT1>ts1, and OnT2<ts1 OnT1<ts1, and OnT2>ts1 OnT1>ts1, OnT2>ts1, t3-t1<ts2, and t4-t2<ts2 Scenario 2 Scenario 3 Scenario 4 Note: 1. ts1 is the threshold of OnT1 and OnT2, and ts2 is the threshold of timestamp differences; t1, t2, t3, t4, OnT1, and OnT2 are the same as defined previously. 2. In this study, ts1 and ts2 are determined as 4.1s and 3.0s, respectively. Identifying Operating status • Under congested traffic, the following algorithm is proposed to identify a vehicle’s operating status. Flowchart for Identifying Vehicle Operating Status

16. New Vehicle Classification Model • When a vehicle is identified to fall into Scenario 1, 2, or 3: • Assumptions: constant acceleration or deceleration over the detection area Where, Lv= length of the detected vehicle (ft); Ls =length of each single loop within the dual-loop (ft); vo= speed of the vehicle entering the upstream loop (M loop) (ft/s); a = vehicle acceleration (ft/s2); and D, t, OnT1, and OnT2 are the same as defined earlier.

17. New Vehicle Classification Model • When a vehicle is identified to fall into Scenario 4: Where, Lv=length of vehicle (ft); Ls = length of each single loop within the dual-loop (ft); tdec= time period from a vehicle entering the M loop to its stop (s); tacc= time period from a vehicle starting to move to leaving the M loop (s); a = the average acceleration rate of vehicles when they start to move under stop-and-go traffic (ft/s2); ts = time period for a vehicle stopping on both loops (s); vmin= the minimum speed which can maintain a vehicle running without stop (ft/s); f1, f2, and f3 = adjusting factors for different vehicle types (in this study, f1= f2= f3=1); D, t, t2, t3, OnT1, and OnT2 = as the same as defined previously.

18. Output of new Model • The new vehicle length estimation model vs. the existing model (Scenario 1, 2, and 3) Error: Existing model: 33.5% New model: 6.7%

19. Output of new Model • The new vehicle length estimation model vs. the existing model (Scenario 4) Error: Existing model: 235% Newmodel: 17.1%

20. Acknowledgement • My advisor Dr. Heng Wei’s instructions during my study at University of Cincinnati • The study is supported by an Ohio Transportation Consortium (OTC) grant • Dr. Ben Coifman at the Ohio State University provided the event dual-loop data • Dr. Zhixia Li, Mr. Zhuo Yao, and other fellows in the ART Engine Lab provide great help in VEVID update and data collection

21. Thank you! Questions?