Risk Assessment Approach of Toll Road Operator

The purpose of the article is to assess the risks of a toll road operator arising in the event of traffic congestion at toll collection points. In the Russian Federation, in infrastructure projects, including toll road projects, the organizational and legal form of public-private partnership is often used. State authorities have the right to control the quality of the road management by the operator; the state can charge penalty points for low operational efficiency, leading to traffic congestion. The return on investment in infrastructure projects is of a long-term nature, therefore, the task of assessing the risks of possible losses by the toll road operator is quite relevant.Materials and methods. The main research tool is simulation modeling in the AnyLogic software environment, the analysis of the data obtained as a result of simulation was carried out in the environment of the statistical package R. The choice of tools is determined by a large number of subjective (sometimes technical) factors that significantly affect the road capacity at toll collection points, but do not lend themselves to strict formalization. Such factors include refusals to read electronic tolls, drivers changing lanes in the toll collection points, lack of money at the time of travel through the automatic toll lane, and others. All such factors are modeled in the AnyLogic environment as random variables with a rich choice of distribution functions and their parameters.Results. A simulation model of a toll collection point at an exit from a toll road has been created to analyze the throughput of a toll booth with various configurations of toll lanes, various levels of user behavior errors and the provision of drivers with electronic means of travel registration. Using the example of a toll collection point for the “Western High-Speed Diameter” motorway, the parameters of traffic congestion that occur when the number of operating toll lanes on the toll collection point decreases are estimated. In the event of congestion, for each configuration, estimates of the number of vehicles in the congestion, the length of the congestion, the waiting time in the queue at the entrance to the toll collection point at different times of the day, and the time of congestion are determined.Conclusion. Based on the results obtained, an assessment of the risks of non-compliance by the road operator with the standard for ensuring throughput can be carried out. To assess the risks of a toll road as a whole, it is advisable to use an individual simulation model for each toll collection point, taking into account the peculiarities of its geographical location, the composition of traffic at the facility, the regularity of user correspondence, as well as the impact of the surrounding transport, logistics and social infrastructure. In pronounced industrial and logistics areas of the city, in the border zones between the city and the region, an additional assessment of traffic intensity may be required to analyze the throughput of toll collection points, taking into account the daily, weekly and seasonal unevenness of the traffic flow.

A Ridesharing Choice Behavioral Equilibrium Model with Users of Heterogeneous Values of Time

Travelers decide whether to participate in ridesharing based on the trade-off between the travel time and the expense. However, it is still unclear how travelers’ values of time affect their ridesharing behaviors on the congested network. To this end, a path-based ridesharing traffic assignment model was proposed by considering travelers’ heterogenous values of time. In the proposed model, travelers are divided into several classes according to their values of time, and travelers in each class choose their travel modes and routes simultaneously which cost the least. Moreover, travelers in different classes could share the same vehicle to complete their trips together in the proposed model. This paper further discusses how the high-occupancy toll lane affects travelers’ ridesharing behaviors. Numerical results show that: (1) travelers with different values of time show differences in their ridesharing behavior; (2) the single-class ridesharing traffic assignment model may miscalculate the ridesharing scale of users; and (3) building high-occupancy toll lanes plays a positive role in promoting ridesharing for travelers with heterogeneous values of time.

Freight Demand Model for Southern California Freeways with Owner–Operator Truck Drivers

This study evaluates the demand for truck-only toll lanes on Southern California freeways with owner–operator truck drivers. The study implemented the stated preference survey method to estimate the value placed by drivers on time, reliability, and safety measures using various scenarios geared towards assessing those values. The project team met face-to-face with owner- operator truck drivers near the Ports of Los Angeles and Long Beach to understand the drivers’ perspectives regarding truck-only toll lanes on Southern California freeways. A data set containing 31 survey responses is obtained and used for statistical data analysis using analysis of variable (ANOVA) and two sample t-tests. The analysis results showed that 75.27% of the owner– operator truck drivers responded are willing to pay toll fees when they choose routes. The tolerated average toll fees are $13.77/ hr and $12.82/hr for weekdays and weekends, respectively. The analysis results also showed that owner–operator truck drivers will take truck-only toll lanes when they take the routes used in four comparisons out of six comparisons according to the three measures such as values of time, reliability, and safety, despite sharing a common origin and destination. The highest toll fee per mile on any day that drivers are willing to pay when the main factor being compared is value of time is $0.31/mile or $18.35/hr. The toll fees associated with reliability and safety measures are $0.30/mile or $8.94/hr and $0.22/mile or $11.01/hr, respectively. These results are meaningful for legislators and transportation agencies as the behaviors and route choice characteristics of owner–operator truck drivers help them better understand the utility and demand for truck-only toll lanes.

VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System

High Occupancy Vehicle/High Occupancy Tolling (HOV/HOT) lanes are operated based on voluntary HOV declarations by drivers. A majority of these declarations are wrong to leverage faster HOV lane speeds illegally. It is a herculean task to manually regulate HOV lanes and identify these violators. Therefore, an automated way of counting the number of people in a car is prudent for fair tolling and for violator detection.In this paper, we propose a Vehicle Passenger Detection System (VPDS) which works by capturing images through Near Infrared (NIR) cameras on the toll lanes and processing them using deep Convolutional Neural Networks (CNN) models. Our system has been deployed in 3 cities over a span of two years and has served roughly 30 million vehicles with an accuracy of 97% which is a remarkable improvement over manual review which is 37% accurate. Our system can generate an accurate report of HOV lane usage which helps policy makers pave the way towards de-congestion.