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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.

This study investigated the impact of converting High Occupancy Vehicle (HOV) lanes to High Occupancy Toll (HOT) lanes on fundamental traffic flow characteristics, focusing on speed, density, and flow relationships. A 25-mile HOV corridor along I-24 Westbound in Nashville, Tennessee was evaluated using both microscopic simulation via VISSIM and data-driven machine learning through a Multi-Layer Perceptron (MLP) neural network. Four operational scenarios were assessed: (1) HOV lanes without enforcement, (2) HOV lanes with effective occupancy enforcement, (3) HOT lanes with limited access points, and (4) HOT lanes with intermediate access points. Flow-density and speed-flow relationships were modeled using Greenshields theory to extract key traffic performance thresholds including free-flow speed, jam density, and maximum flow. Results indicate that while free-flow speeds were generally consistent across scenarios (ranging from 71 to 80 mph), HOV and HOT lanes exhibited higher values compared to general-purpose lanes. Capacity increases were observed following HOV-to-HOT conversions, especially when intermediate access points were introduced. The MLP neural network successfully replicated nonlinear flow relationships and predicted maximum flow near 2000 vph with a jam density of approximately 215 vpmpl—values that closely matched simulation outputs. Both the VISSIM and MLP-derived diagrams demonstrated curve shapes and capacity thresholds that were highly consistent with Highway Capacity Manual (HCM) standards for freeway segments. However, slightly higher thresholds were observed for HOV/HOT lanes, suggesting their potential for improved operational performance under managed conditions. The integration of simulation and machine learning offers a robust framework for evaluating managed lane conversions and informing data-driven policy. Beyond the scenario-specific findings, the study demonstrates an innovative hybrid methodology that links detailed microsimulation with an explainable neural network model, providing a concise and scalable approach for analyzing managed-lane operations. This combined framework highlights the contribution of integrating simulation and AI to enhance the analytical depth and practical relevance of traffic flow studies.

Current transport policies promote better use of existing roadways by using traffic management strategies such as high-occupancy vehicle (HOV) lanes. International experience showed positive mobility impacts of HOV lanes, while research evidence on their safety implications is limited. In Israel, the first HOV lanes were introduced in 2019. This study examined the impacts of HOV lanes on road safety based on a detailed review of international research and accident analyses, which evaluated the safety effects of HOV lanes in Israel. The literature survey applied a systematic screening of research studies from the past two decades and found that HOV lanes were frequently associated with an adverse effect on road safety. Yet, findings were limited to the North American experience, with mostly left-side HOV lanes in use, while in Israel, right-side HOV lanes were introduced. In Israeli evaluations, before-after comparisons of accident changes with comparison groups were applied, with regression models fitted to monthly time series of 17 accident types. Results showed that HOV lanes’ operation led to increasing accident trends, particularly in interchange areas and in the daytime. In injury accidents on road sections, an average increase of 31–41% was found (yet non-significant), while at interchange areas, an increase was even higher and sometimes significant. Thus, adverse safety effects should be expected and accounted for in future planning of HOV lanes. Further research should explore the design features of HOV lanes to reduce their negative safety implications.

Tunnels on freeways, as one of the critical bottlenecks, frequently cause severe congestion and passenger delay. To solve the tunnel bottleneck problem, most of the existing research can be divided into two types. One is to adopt variable speed limits (VSLs) to regulate a predetermined speed for vehicles to get through a bottleneck smoothly. The other is to adopt high-occupancy vehicle (HOV) lane management. In HOV lane management strategies, all traffic is divided into HOVs and low-occupancy vehicles (LOVs). HOVs are vehicles with a driver and one or more passengers. LOVs are vehicles with only a driver. This kind of research can grant priority to HOVs by providing a dedicated HOV lane. However, the existing research cannot both mitigate congestion and maximize passenger-oriented benefits. To address the research gap, this paper leverages connected and automated vehicle (CAV) technologies on intelligent freeways and develops a tunnel bottleneck management strategy with a dynamic HOV lane (DHL). The strategy bears the following features: 1) enables tunnel bottleneck management at a microscopic level; 2) maximizes passenger-oriented benefits; 3) grants priority to HOVs even when the HOV lane is open to LOVs; 4) allocates right-of-way segments for HOVs and LOVs in real time; and 5) performs well in a mixed-traffic environment. The proposed strategy is evaluated through comparison against the non-control baseline and a VSL strategy. Sensitivity analysis is conducted under different congestion levels and penetration rates. The results demonstrate that the proposed strategy outperforms in terms of passenger-oriented delay reduction and HOVs' priority level improvement.

Priority to emergency vehicles, buses, high occupancy vehicles (HOVs), and priced low occupancy vehicles (LOVs) is critical to their efficiency and reliability, where multi-functional exclusive lane (MFEL) serves as a salient element with independent operation environment. To enhance MFEL utility and balance the demand between prioritized and non-prioritized vehicles, this research proposes an optimization model for MFEL design and management to comprehensively serve the trip modes with various priority levels and distinct operation patterns, considering the delay from lane access, bus stop dwelling, signalized intersections, and ride-sharing. Case study follows to calibrate and validate the proposed model under varying passenger demand and emergency vehicle frequency, finding that MFEL may reduce total travel time in all scenarios especially under high traffic demand. Sensitivity analyses test the effect of road lane count and bus occupancy on MFEL design and management, where the road with more lanes is more flexible to accommodate increased passenger demand without increasing HOV critical occupancy or LOV price rate. Moreover, higher bus occupancy assists in avoiding significant increase of LOV price rate to promote trip equity. This research may lay foundation to MFEL implementation to mitigate traffic congestion and promote transport sustainability.

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%.

This study proposes two general alternative designs to enhance the operation and safety of High Occupancy Vehicle (HOV) lanes at junctions with bus terminals or parking lots. A series of analysis tools, including microscopic simulation, video-based vehicle tracking technique, and Surrogate Safety Assessment Model (SSAM), are applied to model and test the safety and operational efficiency of an HOV road segment near a bus terminal in Québec as a case study. A metaheuristic optimization algorithm (i.e., Whale Optimization Algorithm) is employed to calibrate the microscopic model while deviation from the observed headway distribution is considered as a cost function. The results indicate that this type of HOV configurations exhibits significant safety problems (high number of crossing conflicts) and operational issues (high value of total delay) due to the terminal-bound buses that frequently need to travel across the main road. It is shown that the proposed alternative geometry design efficiently ameliorates the traffic conflicts issues. In addition, the alternative control design scheme significantly reduces the public transit delay. It is expected that this methodology can be applied to other reserved lane configurations similar to the investigated case study.

High Occupancy Vehicle (HOV) lanes are widely used on freeways and play an important role in network design and management. Likewise, link performance functions serve as an essential tool for transport system analysis. This paper aims to support network analysis by providing a tailored link performance function for HOV lanes contiguous with general motor lanes on freeways. Specifically, real traffic data is used for model calibration and evaluation that was assembled from the Performance Measurement System (PeMS) maintained by the California Department of Transportation. Three alternative models for link performance functions of HOV lanes on freeways are developed, which take traffic performance on both HOV lanes and adjacent sets of general motor lanes into consideration. To calibrate the parameters of the models, linear regression is made through stepwise and enter methods and nonlinear regression is carried out using sequential quadratic programming. Statistical analysis together with an evaluation using real traffic data is conducted to evaluate the validity of the proposed models. Our results show that all the three proposed models for contiguous HOV lanes on freeways are statistically significant and perform better in representing real traffic condition with regards to a traditional link performance function, with one specific nonlinear model best supported.

Widespread use of single-occupancy cars often leads to traffic congestion. Using anonymized traffic speed data from Android phones collected through Google Maps, we investigated whether high-occupancy vehicle (HOV) policies can combat congestion. We studied Jakarta’s “three-in-one” policy, which required all private cars on two major roads to carry at least three passengers during peak hours. After the policy was abruptly abandoned in April 2016, delays rose from 2.1 to 3.1 minutes per kilometer (min/km) in the morning peak and from 2.8 to 5.3 min/km in the evening peak. The lifting of the policy led to worse traffic throughout the city, even on roads that had never been restricted or at times when restrictions had never been in place. In short, we find that HOV policies can greatly improve traffic conditions.

Plug-in electric vehicles (PEVs) have been commercially available in the global market for about 3 years. Many countries have policies designed to stimulate consumer acceptance and accelerate market adoption. In the United States (U.S.), the biggest PEV market, sales have more than tripled since 2011. During the same period, PEV sales have increased, albeit slowly, in most western European countries. Notably, some European countries, such as Norway, showed strong increases mainly owing to generous incentives to PEV consumers. Japan is the second-largest PEV market in terms of number of vehicles sold. The Nissan battery electric vehicle (BEV) Leaf is the top-selling PEV model, with more than 100,000 units sold globally since its launch in 2010. In contrast, after 3 years of policy stimulation, PEV market share in China is still lower than 0.1 % of total car sales, and most of these vehicles were purchased by either central or local governments. However, PEV bus production in China has increased dramatically over last 3 years. These market trends, together with strong government policies, show that national and regional PEV-related incentives in selected countries can play an important role in jump-starting the PEV market.