High-occupancy Vehicle Lanes Research Articles

Semantic Trajectory Frequent Pattern Mining Model

A method for mining frequent patterns of individual user trajectories is proposed based on location semantics. The semantic trajectory is obtained by inverse geocoding and preprocessed to obtain the Top-k candidate frequent location item sets, and then the spatio-temporal sequence intersection and the divide and conquer merge methods are used to convert the frequent iterative calculation of long itemsets into hierarchical sets' regular operations, the superset and subset of frequent sequences are found. This kind of semantic trajectory frequent pattern mining can actively identify and discover potential carpooling needs, and provide higher accuracy for location-based intelligent recommendations such as carpooling and HOV lane travel (High-Occupancy Vehicle Lane). Carpool matching and recommendation based on semantic trajectory in this paper is suitable for single carpooling and relay-ride carpooling. the results of simulation carpooling experiments prove the applicability and efficiency of the method.

The Impact of High-Occupancy Vehicle Lanes on Carpooling

The Impact of High-Occupancy Vehicle Lanes on Carpooling

Incentivizing Commuters to Carpool: A Large Field Experiment with Waze

Problem definition: Traffic congestion is a serious global issue. A potential solution, which requires zero investment in infrastructure, is to convince solo car users to carpool. Academic/practical relevance: In this paper, we leverage the Waze Carpool service and run the largest ever digital field experiment to nudge commuters to carpool. Methodology: Our field experiment involves more than half a million users across four U.S. states between June 10 and July 3, 2019. We identify users who can save a significant commute time by carpooling through the use of a high-occupancy vehicle (HOV) lane, users who can still use an HOV lane but have a low time saving, and users who do not have access to an HOV lane on their commute. We send them in-app notifications with different framings: mentioning the HOV lane, highlighting the time saving, emphasizing the monetary welcome bonus (for users who do not have access to an HOV lane), and a generic carpool invitation. Results: We find a strong relationship between the affinity to carpool and the potential time saving through an HOV lane. Managerial implications: Specifically, we estimate that mentioning the HOV lane increases the click-through rate (i.e., proportion of users who clicked on the button inviting them to try the carpool service) and the onboarding rate (i.e., proportion of users who signed up and created an account with the carpool service) by 133%–185% and 64%–141%, respectively, relative to a generic invitation. We conclude by discussing the implications of our findings for carpool platforms and public policy. History: This paper has been accepted as part of the 2021 Manufacturing & Service Operations Management Practice-Based Research Competition. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.1033 .

Where Next for HOV Lanes?

In October, the Toll Insight 2021 HOV Technology Conference investigated the evolving technology surrounding high-occupancy vehicle lanes and the ways in which compliance can be enforced. Here, Lev Pinelis, founder of Toll Insight, gives a complete overview of the proceedings

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.

Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review

Carpooling schemes for mutual cost benefits between the driver and the passengers has a long history. However, the convenience of driving alone, the increasing level of car ownership, and the difficulties in finding travelers with matching timing and routes keep car occupancy low. Technology is a key enabler of online platforms which facilitate the ride matching process and lead to an increase in carpooling services. Smart carpooling services may be an alternative and enrichment for mobility, which can help smart cities (SCs) reduce traffic congestion and gas emissions but require the appropriate architecture to support connection with the city infrastructure such as high-occupancy vehicle lanes, parking space, tolls, and the public transportation services. To better understand the evolution of carpooling platforms in SCs, bibliometric analysis of three separate specialized literature collections, combined with a systematic literature review, is performed. It is identified that smart carpooling platforms could generate additional value for participants and SCs. To deliver this value to an SC, a multi-sided platform business model is proposed, suitable for a carpooling service provider with multiple customer segments and partners. Finally, after examining the SC structure, a carpooling platform architecture is presented, which interconnects with the applicable smart city layers.

Retaliatory aggressive driving: A justice perspective

Driving anger and roadway aggression have previously been conceptualized using attributional theory, the theory of planned behavior, and the general aggression model (GAM) framework. The current study builds on these findings, testing the applicability of the attribution-of-blame model of perceptions of injustice and expanding existing models of retaliatory driving aggression to include unjust world beliefs and sensitivity to injustice. A sample of 269 participants from a large urban Canadian university viewed five animated driving scenarios (i.e., a queuing violation, a dangerous turn in front of oncoming traffic, selfish parking behavior, misuse of a high occupancy vehicle lane, and a driver failing to stop at a red light). Prior to viewing each scenario, a brief written description of the scenario was provided to each participant and read aloud by the experimenter. After viewing each scenario, participants completed a questionnaire regarding their attributions, emotions, and anticipated behavior in response to the animated scenario. After viewing all animated videos, participants completed a second questionnaire that assessed individual differences and demographic variables. Consistent with the GAM, structural equation and mediation analyses identified a significant path from individual differences (i.e., belief in an unjust world and driving injustice sensitivity), through internal states (i.e., perceptions of injustice and anger), to retaliatory aggressive driving. Results of this study found consistent paths between factors which were significant across all five scenarios and may therefore be generalizable to other driving situations. Other pathways were found to influence only a selection of the five scenarios, suggesting that they may be situation specific. Results provide support for possible intervention strategies that can be employed by driver education programs to reduce aggressive driving.

Slugging: Casual Carpooling for Urban Transit

Problem definition: “Slugging,” or casual carpooling, refers to the commuting practice of drivers picking up passengers at designated locations and offering them a free ride in order to qualify for high-occupancy vehicle (HOV) lanes. Academic/practical relevance: It is estimated that tens of thousands of daily commuters rely on slugging to go to work in major U.S. cities. As drivers save commute time and passengers ride for free, slugging can be a promising Smart Mobility solution. However, little is known about the welfare, policy, and environmental implications of slugging. Methodology: We develop a stylized model that captures the essence of slugging. We characterize commuters’ equilibrium behavior in the model. Results: We find that slugging indeed makes commuters better off. However, the widely observed free-ride tradition is socially suboptimal. As compared with the social optimum, commuters always underslug in the free-slugging equilibrium when highway travel time is insensitive to slugging activities but may overslug otherwise. The socially optimal outcome can be achieved by allowing pecuniary exchanges between drivers and passengers. Interestingly, passengers may be better off if they pay for a ride than if they do not under free slugging. We also find that although policy initiatives to expand highway capacity or improve public transportation always increase social welfare in the absence of slugging, they may reduce social welfare in areas where free slugging is a major commuting choice. Nevertheless, these unintended consequences would be mitigated by the introduction of pecuniary exchanges. Finally, contrary to conventional wisdom, slugging as a form of carpooling can result in more cars on the road and thus, more carbon emissions. Managerial implications: Our results call upon the slugging community to rethink the free-ride practice. We also caution that slugging benefits commuters possibly to the detriment of the environment.

Modeling and managing the morning commute problem with park-and-ride-sharing

In this paper, park-and-ride-sharing (P&RS) is proposed to manage morning peak hour congestion in a monocentric linear city, where there is a multilane highway with a single bottleneck connecting the residential area and the central business district (CBD). To realize ride-sharing matching, a ride-sharing parking lot (RSPL) and a ride-sharing meeting point (RSMP) are set up somewhere between the origin and the bottleneck. To encourage ride-sharing participation, the lanes of the highway are physically divided into high occupancy vehicle (HOV) and general purpose (GP) lanes for only ride-sharing vehicles and non-ride-sharing vehicles, respectively. The user equilibrium principle in terms of generalized travel cost is adopted to formulate commuters’ mode choice and arrival time choice at the RSMP. It is demonstrated that there exist three possible equilibrium ride-sharing matching patterns and three possible equilibrium traffic flow patterns. All possible equilibrium ride-sharing matching and traffic flow patterns are analytically derived. In addition, to minimize total system cost (TSC), three break-even parking charge schemes are designed under the case that all parking lots are managed by the government. The optimal allocation scheme of bottleneck capacity to HOV lanes under each parking charge scheme is analytically derived. Finally, numerical examples are provided to illustrate the properties of the proposed bottleneck model with P&RS and the effectiveness of the designed parking charge schemes.

Entrance Location Planning of HOV Lanes in Multi-lane Highway Considering System and HOV Lane State

The high-occupancy vehicle (HOV) lane is an effective traffic demand management that encourages carpooling. In this study, we examine entrance location planning of HOV lanes. A highway commuting system containing HOV lanes is proposed. Commuters make travel choices based on the principle of minimum commuting time, i.e., the system is in the user equilibrium state. By planning the HOV lanes’ entrance location, the manager indirectly influences the commuters’ travel choices, thus achieving the predetermined management goals. In this study, an optimization model is developed where the decision variable is the entrance location of the HOV lane, and the conditional constraint of user equilibrium is satisfied. The objective function is a weighted average of a system state indicator and an HOV lane state one. Numerical examples show that the proposed method solves the location planning problem of the HOV lane entrance. The quantitative results reveal that the system congestion is relieved when the HOV lane entrance is close to the city center, and the traffic condition of the HOV lane is improved when the entrance is close to the city’s edge.

Time, Comfort, and Preferences for Shared Transit

We evaluate both a natural field experiment and quasi-experimental variation in commute times for a shared transportation service provided by a large technology company during morning and afternoon commutes. We find that reduced commute times of roughly 2.5 minutes through expanded high occupancy vehicle (HOV) lanes and brand new coaches both significantly increased ridership by roughly 5.5%. We leverage these insights to describe some insights for public transit designers. We also highlight the importance of correctly specifying standard errors in randomized control trials (RCTs) with a small number of clusters: p-values increase by multiple orders of magnitudes when appropriately bootstrapping standard errors.

Developing safety performance functions for freeways at different aggregation levels using multi-state microscopic traffic detector data

Safety Performance Functions (SPFs) have been widely used by researchers and practitioners to conduct roadway safety evaluation. Traditional SPFs are usually developed by using annual average daily traffic (AADT) along with geometric characteristics. However, the high level of aggregation may lead to a failure to capture the temporal variation in traffic characteristics (e.g., traffic volume and speed) and crash frequencies. In this study, SPFs at different aggregation levels were developed based on microscopic traffic detector data from California, Florida, and Virginia. More specifically, five aggregation levels were considered: (1) annual average weekday hourly traffic (AAWDHT), (2) annual average weekend hourly traffic (AAWEHT), (3) annual average weekday peak/off-peak traffic (AAWDPT), (4) annual average day of the week traffic (AADOWT), and (5) annual average daily traffic (AADT). Model estimation results showed that the segment length and volume, as exposure variables, are significant across all the aggregation levels. Average speed is significant with a negative coefficient, and the standard deviation of speed was found to be positively associated with the crash frequency. It is noteworthy that the operation of the high occupancy vehicle (HOV) lanes was found to have a positive effect on crash frequency across all the aggregation levels. The model results also showed that the AAWDPT and AADOWT models consistently performed better (the improvements range from 3.14%–16.20%) than the AADT-based SPF, which implies that the differences between the day of the week and peak/off-peak periods should be considered in the development of crash prediction models. The model transferability results indicated that the SPFs between Florida and Virginia are transferrable, while the models between California and the other two states are not transferrable.

Optimizing carpool formation along high-occupancy vehicle lanes

High-occupancy vehicle (HOV) lanes are restricted traffic lanes that are reserved for vehicles with multiple car occupants. Depending on the current number of passengers, a driver must either travel slower on the often-congested general-purpose lane or can access the faster HOV lane. In this paper, we provide optimization approaches for matching supply and demand when building carpools along HOV lanes. In current applications, carpools form spontaneously in slugging areas where potential passengers queue. However, internet-enabled mobile phones that are connected to a central ride sharing platform enable dynamic carpool formation based on sophisticated scheduling procedures. We investigate various versions of the carpool formation problem. The computational complexity is analyzed in depth, and suitable solution procedures are developed. These procedures are applied to quantify the benefit of an optimized carpool formation process. In a comprehensive computational study, we compare our optimization approaches with spontaneous ride sharing and show that substantially better solutions for all stakeholders can be obtained.

Understanding the formation of rubbernecking queues on urban freeways

Rubbernecking on freeways is a common problem that arises when queuing traffic develops in the opposite direction of a freeway incident. The goals of this study are to evaluate the effects of rubbernecking on traffic, investigate incident and freeway characteristics associated with it, evaluate rubbernecking models, and analyze drivers’ behavior in relationship to rubbernecking. To this end, traffic data around a major incident were analyzed. Results revealed that rubbernecking can instigate significant queue lengths, congestion durations, and traffic delays. In addition, analysis of 637 incidents revealed that 12% of incidents instigated rubbernecking queues. A logistic regression model was also fitted to the data. Results from the model suggest that on-ramps, high-occupancy vehicle (HOV) lanes, and percent of trucks are factors associated with rubbernecking. In addition, validation analyses of rubbernecking models pointed to limitations of estimated statistical models in effectively predicting rubbernecking events. Furthermore, results from a drivers’ behavior self-reporting survey suggest that a majority of drivers tend to decrease their speed around an incident in the opposite direction of traffic. It was also observed that respondents attributed these actions to habits and behavior more frequently than incident and freeway characteristics. Findings from this work can be used to help develop targeted rubbernecking traffic control measures, enhance rubbernecking data collection methods and modeling, and advance behavioral interventions to reduce the onset of rubbernecking queues on freeways.

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.

High-occupancy vehicle lane management with tradable credit scheme: An equilibrium analysis

The tradable credit scheme (TCS), as a revenue-neutral management measure, has received much attention in the field of traffic management in recent years. This paper investigates the management performance of the TCS on high-occupancy vehicle (HOV) lanes with the approach of equilibrium analysis. The analysis is based on a commuting scenario in which commuters use a multi-lane highway with HOV lanes. We developed a basic system in which commuters have two alternative travel modes: driving alone in general-purpose (GP) lanes or carpooling in HOV lanes. Under this assumption, the HOV lane management with TCSs is formulated as a mathematical programming problem. We analyze the effects with respect to feasible TCSs, effective TCSs, and optimal TCSs, respectively. We extend the basic system by considering regular carpoolers with the scenario of HOVs choosing GP lanes. Under this situation, the HOV lane management with TCSs is formulated as a variational inequality problem. We further analyze the management effects of different TCSs. Numerical examples demonstrate system indicators with several typical states, the impact of key parameters on TCS management performance, and the feature of the extended system in typical scenarios. Results show that the optimal TCS is achievable in the basic system by limiting the traffic flow of GP lanes. However, the optimal TCS may not exist in the extended system considering regular carpoolers, and a sufficient condition is proposed to guarantee its existence.

Research on HOV Lane Priority Dynamic Control under Connected Vehicle Environment

The optimization of high-occupancy vehicle (HOV) lane management can better improve the efficiency of road resources. This paper first summarized the current research on HOV lane implementation and analyzed and identifies the threshold of setting road HOV lane dynamic control under the connected vehicle environment. Then, the HOV lane priority dynamic control process was determined, and the operating efficiency and energy consumption evaluation method was proposed. Moreover, a case study in Wuxi City, China, was carried out. The results showed that, after implementing the HOV lane priority dynamic control, the total mileage of road network vehicles was saved by 4.93%, the average travel time per capita was reduced by 4.27%, and the total energy-saving rate of road network travel was 21.96%.

Dynamic equilibrium commuting in a multilane system with ridesharing

High-occupancy vehicle (HOV) lane has been designed to maximize the movement of people rather than vehicles. For completely understanding the travel pattern of a multilane highway system, this paper presents a dynamic user equilibrium framework, in which commuters, composed of solo-driving drivers, ridesharing drivers and ridesharing passengers, choose their departure times and transport modes to workplace every day. The ridesharing vehicles can use both the HOV lane and the general purpose lane while the solo drivers are only allowed to use the latter. It is found that the rush hour on both lanes is identical but the system’s total travel cost decreases, in a nonlinear way, with the expansion of HOV lane’s capacity.

The Impact of High-Occupancy Vehicle Lanes on Commuters: Field Evidence

Governments are actively investing to alleviate traffic congestion. Starting from the 1970s, a common policy has been to introduce high-occupancy vehicle (HOV) lanes, that is, traffic lanes exclusively reserved for vehicles with more than one occupant. Following the introduction of HOV lanes in several countries, their effectiveness has been the topic of heated debates. In this paper, we have a unique opportunity to empirically examine the impact of introducing HOV lanes on carpooling. In October 2019, the Israeli government has decided to introduce three HOV lanes. In this context, we have access to traffic and carpool data — from Waze (the free GPS navigation app owned by Google) — both before and after introducing the HOV lanes. We can thus rigorously quantify the impact of introducing an HOV lane on carpool intent and adoption. We also study refined questions around the effectiveness of different types of HOV lanes (2+ versus 3+), the impact at different times of the day, and the behavioral effect on commuters (e.g., strategically adapting commute times). Our study shows that the introduction of the HOV lanes led to a median time saving during rush hours of 5.7-15.7 minutes and increased the carpool rates by hundreds of percent for some routes. Interestingly, we find that the new HOV lanes have a global impact as they also raised the carpool rates for routes unaffected by the HOV lanes. This effect can be explained by the increased awareness of the public about the opportunity of carpooling.

Optimal merging into a high-speed lane dedicated to connected autonomous vehicles

In the future, High Occupancy Vehicle (HOV) lanes or other dedicated lanes might be restricted to autonomous vehicles, e.g wirelessly connected vehicles with longitudinal motion control. These vehicles would likely travel at high speeds in platoons. New criteria for the merging of a vehicle from an adjacent freeway are proposed. To reduce disruption to the flow, only merges into gaps between platoons, not within, are considered. To minimize HOV lane trip time and vehicle deceleration, the optimal merge position is determined from simulations of linear combinations of the deviation of the headway from equilibrium and vehicle velocity differences. These are between the merging vehicle and the trailing vehicle (on the HOV lane) and the merging vehicle and the last vehicle of the preceding platoon. The merging vehicle, due to acceleration limitations on the freeway, generally will merge at a significantly lower velocity than the HOV lane average velocity.