Identifying Wrong-Way Driving Hotspots by Modeling Crash Risk and Assessing Duration of Wrong-Way Driving Events

2017 ◽  
Vol 2616 (1) ◽  
pp. 58-68 ◽  
Author(s):  
Adrian Sandt ◽  
Haitham Al-Deek ◽  
John H. Rogers
Keyword(s):  
Traffic Management ◽  
South Florida ◽  
Crash Risk ◽  
Poisson Regression Model ◽  
Citation Data ◽  
Central Florida ◽  
Traffic Volumes ◽  
Transportation Agencies ◽  
Operational Data

Because wrong-way driving (WWD) crashes are often severe, it is important for transportation agencies to identify WWD hotspot segments appropriate for potential implementation of advanced WWD countermeasures. Two approaches to identify these hotspot segments were developed and applied to a case study of limited-access highways in Central Florida. The first approach used a Poisson regression model that predicted the number of WWD crashes in a roadway segment based on WWD citations, 911 calls, traffic volumes, and interchange designs. Combining this predicted crash value with the actual number of WWD crashes in the segment gave the WWD crash risk of the segment. Ranking roadway segments by WWD crash risk provided agencies with an understanding of which segments had high WWD crash frequencies and high potential for future WWD crashes. This approach was previously applied to South Florida; the research presented here extended this approach to Central Florida. The second approach was based on operational data collected in traffic management centers and could be used if accurate WWD 911 and citation data with GPS location were not available or as a supplement to the first approach. The approach identified and ranked WWD hotspots on the basis of the reported duration of WWD events. In Central Florida, the results of the two approaches agreed with each other and were used by agencies to decide where to implement advanced WWD countermeasures. Together, these approaches can help transportation agencies determine regional WWD hotspots and cooperate to implement advanced WWD countermeasures at these locations.

Benefit–Cost Analyses of Rectangular Flashing Beacon Wrong-Way Driving Countermeasures on Toll Road Exit Ramps in Florida

2019 ◽  
Vol 2674 (1) ◽  
pp. 11-21
Author(s):  
Adrian Sandt ◽  
Haitham Al-Deek ◽  
Md Imrul Kayes ◽  
Patrick Blue ◽  
Valentina Gamero
Keyword(s):  
Crash Risk ◽  
Cost Analyses ◽  
Crash Data ◽  
Central Florida ◽  
Toll Road ◽  
Financial Benefits ◽  
Traffic Volumes ◽  
The Individual ◽  
Benefit Cost ◽  
Measures Of Effectiveness

In recent years, rectangular flashing beacons (RFBs) and other technologies have been used as wrong-way driving (WWD) countermeasures on limited access facilities. Studies have shown that these devices effectively reduce WWD, but no research has compared the financial benefits and costs of these countermeasures. Three different methodologies were used to conduct benefit–cost analyses for RFB WWD countermeasures installed on Central Florida toll road exit ramps. The studied benefits included savings from reductions in WWD crashes, non-crash events, and injuries, whereas costs included equipment, installation, and maintenance costs. For the first two methodologies, the reduction in WWD crash risk (WWCR) at the RFB-equipped ramps was determined. This WWCR considered non-crash WWD events, interchange design, and traffic volumes. Different measures of effectiveness (turn-around percentage of detected wrong-way vehicles at the RFB ramps and reduction in WWD 911 calls and citations at the RFB interchanges compared with similar comparison interchanges without RFBs) were used in these two methodologies to estimate the WWCR reduction and associated savings. For the third methodology, the relationship between WWD crashes and non-crash events was used to determine the average savings for WWD 911 calls and citations. Before–after analyses were then conducted to determine the individual reductions in WWD 911 calls and citations. Applying these three methods resulted in life-cycle benefit–cost ratios ranging from 2.49 to 4.10 (crash savings) and from 4.77 to 7.20 (injury savings). Other agencies could use these methodologies to determine the benefits of WWD countermeasures or other technologies with limited crash data.

scholarly journals Automated Machine Learning Pipeline for Traffic Count Prediction

Modelling ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 482-513
Author(s):  
Amirsaman Mahdavian ◽  
Alireza Shojaei ◽  
Milad Salem ◽  
Haluk Laman ◽  
Jiann-Shiun Yuan ◽  
...  
Keyword(s):  
Traffic Management ◽  
Linear Models ◽  
Nonlinear Models ◽  
Single Case ◽  
Feature Selection Method ◽  
Optimization Techniques ◽  
Percentage Error ◽  
Traffic Count ◽  
Traffic Volumes

Research indicates that the projection of traffic volumes is a valuable tool for traffic management. However, few studies have examined the application of a universal automated framework for car traffic volume prediction. Within this limited literature, studies using broad data sets and inclusive predictors have been inadequate; such works have not incorporated a comprehensive set of linear and nonlinear algorithms utilizing a robust cross-validation approach. The proposed model pipeline introduced in this study automatically identifies the most appropriate feature-selection method and modeling approach to reduce the mean absolute percentage error. We utilized hyperparameter optimization to generate a universal automated framework, distinct from model optimization techniques that rely on a single case study. The resulting model can be independently customized to any respective project. Automating much of this process minimizes the work and expertise required for traffic count forecasting. To test the applicability of our models, we used Florida historical traffic data from between 2001 and 2017. The results confirmed that nonlinear models outperformed linear models in predicting passenger vehicles’ monthly traffic volumes in this specific case study. By employing the framework developed in this study, transportation planners could identify the critical links on US roads that incur overcapacity issues.

Analysis of Performance Data Collected from Two Wrong-Way Driving Advanced Technology Countermeasures and Results of Countermeasures Stakeholder Surveys

2018 ◽  
Vol 2672 (14) ◽  
pp. 96-105 ◽  
Author(s):  
Md Imrul Kayes ◽  
Haitham Al-Deek ◽  
Adrian Sandt ◽  
John H. Rogers ◽  
Grady Carrick
Keyword(s):  
Light Emitting Diode ◽  
South Florida ◽  
Performance Data ◽  
Advanced Technology ◽  
Light Emitting ◽  
Central Florida ◽  
Survey Results ◽  
Serious Injuries ◽  
Transportation Agencies ◽  
Analysis Of Performance

Wrong-way driving (WWD) often leads to severe collisions that cause serious injuries and deaths. Conventional “Wrong Way” signs can reduce WWD events, but can be insufficient in some cases. In areas with many WWD events, transportation agencies can be proactive by considering the use of countermeasures with advanced technologies to actively warn motorists of WWD violations. This paper analyzes recent performance data collected from two types of advanced technology WWD countermeasures implemented in Florida: light-emitting diode (LED) signs in South Florida and rectangular flashing beacon (RFB) signs in Central Florida. The 17 LED sites experienced a 38% reduction in WWD citations and 911 calls after the signs were installed. Images taken by the on-site cameras were examined to see how many vehicles turned around for both the RFB and LED treatment sites. Over 77% of the 170-detected wrong-way vehicles self-corrected their wrong-way movement at the RFB sites (each with two sets of signs and multiple cameras) and 14% self-corrected at the LED sites (each with one set of signs and one camera). Surveys were also conducted regarding these two WWD countermeasures. More than 73% of the 2,052 respondents preferred RFBs over LEDs, mainly due to the double set of RFB signs and their flashing pattern. The performance and survey results show that both the LEDs and RFBs have effectively reduced WWD movements. However, modifications could be made to both countermeasures to improve their detection ability and make wrong-way drivers more likely to turn around.

scholarly journals Analysis of the System of Controlling Paid Parking Zones

2021 ◽  
Vol 13 (8) ◽  
pp. 4211
Author(s):  
Maciej Kozłowski ◽  
Andrzej Czerepicki ◽  
Piotr Jaskowski ◽  
Kamil Aniszewski
Keyword(s):  
Control System ◽  
Smart City ◽  
Measurement Errors ◽  
Fundamental Problem ◽  
Urban Traffic ◽  
Statistical Dependence ◽  
Actual State ◽  
Event Time ◽  
Operational Data

Urban traffic can be curbed in various ways, for instance, by introducing paid unguarded parking zones (PUPZ). The operational functionality of this system depends on whether or not the various system features used to document parking cases function properly, including those which enable positioning of vehicles parked in the PUPZ, recognition of plate numbers, event time recording, and the correct anonymisation of persons and other vehicles. The most fundamental problem of this system is its reliability, understood as the conformity of control results with the actual state of matters. This characteristic can be studied empirically, and this article addresses the methodology proposed for such an examination, discussed against a case study. The authors have analysed the statistical dependence of the e-control system’s measurement errors based on operational data. The results of this analysis confirm the rationale behind the deployment of the e-control system under the implementation of the smart city concept in Warsaw.

scholarly journals A Case Study in View of Developing Predictive Models for Water Supply System Management

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3305
Author(s):  
Katarzyna Pietrucha-Urbanik ◽  
Barbara Tchórzewska-Cieślak ◽  
Mohamed Eid
Keyword(s):  
Water Supply ◽  
Predictive Models ◽  
Failure Modes ◽  
Supply System ◽  
Water Supply System ◽  
System Management ◽  
System A ◽  
Operational Failure ◽  
Operational Data

Initiated by a case study to assess the effectiveness of the modernisation actions undertaken in a water supply system, some R&D activities were conducted to construct a global predictive model, based on the available operational failure and recovery data. The available operational data, regarding the water supply system, are the pipes’ diameter, failure modes, materials, functional conditions, seasonality, and the number of failures and time-to-recover intervals. The operational data are provided by the water company responsible of the supply system. A predictive global model is proposed based on the output of the operational data statistical assessment. It should assess the expected effectiveness of decisions taken in support of the modernisation and the extension plan.

scholarly journals Evaluation of the Applicability of the Ramp Metering to a Freeway Using Microsimulation: A Case Study in Riyadh, Saudi Arabia

2019 ◽  
Vol 9 (20) ◽  
pp. 4406
Author(s):  
Seongkwan Lee ◽  
Amr Shokri ◽  
Abdullah Al-Mansour
Keyword(s):  
Saudi Arabia ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Fixed Time ◽  
Ramp Metering ◽  
Traffic Data ◽  
Geometrical Data ◽  
Overall Performance ◽  
Length Reduction

Riyadh, the capital of Saudi Arabia, suffers from traffic congestion like other modern societies, during peak hours but also all day long, even without any incidents. To solve this horrible traffic congestion problem, various efforts have been made from the Active Traffic Management (ATM) aspect. Ramp metering (RM) is one of the representative methods of the ATM and has already proven its value in many locations worldwide. Unfortunately, RM has not yet been fully implemented in Saudi Arabia. This research aimed to assess the applicability of RM to a freeway in Riyadh using microsimulation. The widely known software VISSIM (PTV Planung Transport Verkehr AG, Germany, 1992) was chosen to compare the performances of various RM operating scenarios, such as fixedtime operation with different sub-scenarios and traffic-responsive operation using ALINEA (Asservissement Lineaire d’entree Autoroutiere) algorithm. For the simulations, this study targeted Makkah Road, one of the major freeways in Riyadh, and collected geometrical data and traffic data from that freeway. Analysis of four main scenarios and eight sub-scenarios, proved that overall performance of the fixed-time RM operation is generally good. The sub-scenario 4V3R of the fixed-time RM operation was the best in average queue length reduction. However, the traffic-responsive operation was best in average speed improvement.

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