Research on Decision Evaluation Model of HOV Lane Setting

In order to achieve the purpose of improving the travel efficiency of commuters in the periphery of the city, expanding the beneficiary groups of urban rail transit, and alleviating urban road traffic congestion, when planning and setting up HOV in the periphery of the city, it is necessary to analyze the feasibility of HOV lane setting from both the demand conditions and the setting conditions. This paper combines machine learning to construct a decision-making evaluation model for HOV lane setting and studies the optimal layout model and algorithm of HOV lanes in service rail transit commuter chain. The setting, planning, and layout of HOV lanes are a two-way interactive process of traveler's path selection and designer's road planning. Finally, after the model is constructed, the performance of the system model is verified. The results show that the system studied in this paper can be used for traffic data and lane planning analysis. Therefore, in the process of urban operation, the HOV model constructed in this paper is mainly used to alleviate urban traffic and improve urban operation efficiency.

High-Occupancy Vehicle Lane Enforcement System

Introduction: An automatic High-Occupancy Vehicle (HOV) lane enforcement system is developed and evaluated. Current manual enforcement practices by the police bring about safety concerns and unnecessary traffic delays. Only vehicles with more than five passengers are permitted to use HOV lanes on freeways in Korea. Hence, detecting the number of passengers in HOVs is a core element for their development. Methods: For a quick detection capability, a YOLO-based passenger detection model was built. The system comprises three infrared cameras: two are for compartment detection and the other is for number plate recognition. Multiple infrared illuminations with the same frequency as the cameras and laser sensors for vehicle detection and speed measurement are also employed. Results: The performance of the developed system is evaluated with real-world data collected on proving ground. As a result, it showed a passenger detection error of nine percent on average. The performances revealed no difference in vehicle speeds and the number of passengers according to ANOVA tests. Conclusion: Using the developed system, more efficient and safer HOV lane enforcement practices can be made.

HOV access location analysis at Erin Mills Parkway and Highway 403

High Occupancy Vehicle (HOV) lanes are one [of] the most commonly used methods to reduce the number of vehicles on the road network. HOV provides a faster and reliable option to single occupancy vehicles, thus inducing more people to car pool. The success of HOV lanes depends on the reduction of travel time and increased trip reliability. Therefore, in order to reduce travel time and improve trip reliability this study emphasizes on the HOV access location relative to an access ramp. In this case we have chosen the interchange at Erin Mills Parkway and Highway 403 as a subject of our study. The study was divided into 2 parts, namely the field review and simulation of different options in order to optimize the HOV access location. During the field review it was found that 75% of the vehicles are in a position to enter the HOV lane 200m upstream of the exiting access location. Also, approximately 35% of vehicles were jumping the buffer before the start of the access location. In the second part of the study a total of 6 options were explored using VISSIM micro simulation software. The results of the simulation showed that the access location 200m downstream of the Speed Change Lane with a total access length of 600m is the best option. In addition, it was found that buffer separated HOV lane operate better then HOV lane without a buffer zone. This can be attributed to the increase of HOV lane for short trips, which increases the traffic volume on the HOV lane.

HOV access location analysis at Erin Mills Parkway and Highway 403

High Occupancy Vehicle (HOV) lanes are one [of] the most commonly used methods to reduce the number of vehicles on the road network. HOV provides a faster and reliable option to single occupancy vehicles, thus inducing more people to car pool. The success of HOV lanes depends on the reduction of travel time and increased trip reliability. Therefore, in order to reduce travel time and improve trip reliability this study emphasizes on the HOV access location relative to an access ramp. In this case we have chosen the interchange at Erin Mills Parkway and Highway 403 as a subject of our study. The study was divided into 2 parts, namely the field review and simulation of different options in order to optimize the HOV access location. During the field review it was found that 75% of the vehicles are in a position to enter the HOV lane 200m upstream of the exiting access location. Also, approximately 35% of vehicles were jumping the buffer before the start of the access location. In the second part of the study a total of 6 options were explored using VISSIM micro simulation software. The results of the simulation showed that the access location 200m downstream of the Speed Change Lane with a total access length of 600m is the best option. In addition, it was found that buffer separated HOV lane operate better then HOV lane without a buffer zone. This can be attributed to the increase of HOV lane for short trips, which increases the traffic volume on the HOV lane.

A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method

High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.

Sustainable Commuting: Results from a Social Approach and International Evidence on Carpooling

Sustainable commuting (SC) usually refers to environmentally friendly travel modes, such as public transport (bus, tram, subway, light rail), walking, cycling, and carpooling. The double aim of the paper is to summarize relevant prior results in commuting from a social approach, and to provide new, international empirical evidence on carpooling as a specific mode of sustainable commuting. The literature shows that certain socio-demographic characteristics clearly affect the use of non-motorized alternatives, and compared to driving, well-being is greater for those using active travel or public transport. Additionally, this paper analyzes the behavior of carpooling for commuting, using ordinary least squares (OLS) models, which have been estimated from the Multinational Time Use Study (MTUS) for the following countries: Bulgaria, Canada, Spain, Finland, France, Hungary, Italy, South Korea, the United Kingdom, and the United States. Results indicate that carpooling for commuting is not habitual for workers, as less than 25% of the total time from/to work by car is done with others on board. With respect to the role of the socio-demographic characteristics of individuals, our evidence indicates that age, gender, education, being native, and household composition may have a cross-country, consistent relationship with carpooling participation. Given that socializing is the main reason for carpooling, in the current COVID-19 pandemic, carpooling may be decreasing and, consequently, initiatives have been launched to show that carpooling is a necessary way to avoid crowded modes of transport. Thus, the development of high-occupancy-vehicle (HOV) lanes by local authorities can increase carpooling, and draw attention to the economic and environmental benefits of carpooling for potential users.

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.

Why Do They Ride with Others? Meta-Analysis of Factors Influencing Travelers to Carpool

Carpooling can be viewed as a simple intervention to reduce congestion, environmental problems, and land use for parking spaces. The present study assembled 18 studies on carpooling from all over the world that were published during the last five years (2014–2018) for a meta-analysis. By calculating effect sizes of 20 different factors, the study aimed to understand user characteristics, motives, and barriers to carpooling, and to gain insights about carpool interventions. Our results indicate that carpooling is very weakly related to socio-demographic variables, and that psychological factors are becoming more important, including monetary and time benefits, reducing congestion, and environmental concerns. Policy-makers can increase carpooling by offering cheaper parking or special parking spaces for carpoolers and introducing high-occupancy vehicle (HOV) lanes. Not surprisingly, fuel prices influence mode choice. The overall findings support previous results, but we found judgmental factors becoming more important for the choice to carpool. We conclude that carpooling services still fail to include many potential users and to serve users adequately. The challenge of meeting the needs of all users requires new approaches to designing carpool concepts, systems, and encounters.