Single Occupancy Vehicles Research Articles

Simulation Approach to Estimate the Potential of High-Occupancy Toll Lanes on German Freeways

This paper focuses on the potential of HOT lanes (high-occupancy toll lanes) in Germany. For this, various scenarios were defined and simulated on a synthetic road network that adheres to German guidelines. The results demonstrated that HOT lanes offered travel time advantages for carpoolers and HOT vehicles but led to longer travel times for single-occupancy vehicles and trucks. Scenarios with higher costs per kilometer generally reinforced the promotion of high-occupancy vehicles and encouraged a shift toward public transport. In contrast, low cost rates had a negative impact on the desired modal split, as the low cost of using the HOT lane became an incentive for comfortable HOT travel rather than carpooling or using public transport. With regard to CO2 and NOx emissions, we demonstrated the unexpected consequences of HOT lanes, when the general traffic flow worsened. In all scenarios, the emissions for both groups were consistently higher than in the baseline scenario without a managed lane, because of the reduced traffic flow. Overall, scenarios with higher costs per kilometer were more favorable in relation to emissions, as the displacement effect toward carpooling vehicles and public transport reduced the overall number of vehicles in the system. This issue may need to be addressed with the implementation of intelligent traffic management systems, especially those that can influence speed. Taking a comprehensive view of the results, HOT lanes in Germany offer the potential for improving traffic conditions and optimizing modal split. However, the limitations of their application must be taken into account.

Exploring the willingness to pay for high-occupancy toll lanes under conditions of low familiarity

This study evaluates details of a progressive transportation development model, wherein high occupancy vehicle (HOV) lanes are transformed into high-occupancy toll (HOT) lanes. Although the management strategy of HOT lanes has been implemented for many years, the implementation of HOT lanes is limited in many regions, and thus, many people remain unfamiliar with the concept. This study aims to evaluate travelers' willingness to pay for HOT lanes based on a “low application” scenario in Shenzhen, China, wherein some HOV lanes are present and may potentially be transformed into HOT lanes, but not all area residents are aware of their intended purpose. According to the Diffusion of Innovation Theory, we leverage this situation to conduct a survey of perceptions and attitudes toward the HOT lanes and evaluate how these perspectives may translate to HOT lane utilization. We further proposed a hybrid utility and regret model considering the impacts of loss aversion caused by tolls or coordinated carpooling for single occupancy vehicles to explore relationships between travel behavior and surveyed demographic and socio-economic characteristics in the context of HOT lanes in places where they are unfamiliar. A sensitivity analysis is conducted to characterize any apparent changing influences. The results show that the model considering decision regret performs well, especially in the scenario with low tolls. Nearly 80% of travelers are willing to pay tolls for HOT lanes, even with limited prior exposure to the concept. The increased acceptance of HOT lanes can be expected to hurt total toll revenue, which may be triggered by easier access to carpool partners associated with the free pass. The study informs a deeper understanding of travelers’ willingness to pay for HOT lanes and helps policy-makers develop differentiated tolling strategies.

HumanLight: Incentivizing ridesharing via human-centric deep reinforcement learning in traffic signal control

Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in information technologies create opportunities for smart solutions that incentivize ridesharing and mode shift to higher occupancy vehicles (HOVs) to achieve the car lighter vision of cities. In this study, we present HumanLight, a novel decentralized adaptive traffic signal control algorithm designed to optimize people throughput at intersections. Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level. By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times. Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window. The proposed algorithm showcases significant headroom and scalability in different network configurations considering multimodal vehicle splits at various scenarios of HOV adoption. Improvements in person delays and queues range from 15% to over 55% compared to vehicle-level SOTA controllers. We quantify the impact of incorporating active vehicles in the formulation of our RL model for different network structures. HumanLight also enables regulation of the aggressiveness of the HOV prioritization. The impact of parameter setting on the generated phase profile is investigated as a key component of acyclic signal controllers affecting pedestrian waiting times. HumanLight’s scalable, decentralized design can reshape the resolution of traffic management to be more human-centric and empower policies that incentivize ridesharing and public transit systems.

SOCIAL NORM AND ENVIRONMENTAL CONCERN AS THE PREDICTORS OF CITIZENS’ ACTUAL BEHAVIOUR TO ADOPT PUBLIC TRANSPORT IN TERENGGANU, MALAYSIA

The increasing penetration of private motor vehicles for commuting demonstrates an astonishing vehicle reliance in Malaysia. The use of public transport could provide a promising alternative by enabling accessibility, reducing congestion and fatalities, and mitigating environmental problems especially in densely populated areas. However, the utilisation of public transport does not depend only on the intention but the actual use as well. Understanding individuals' readiness to adopt public transport are critical for improving sustainable mobility. Based on the theory of Interpersonal Behaviour (TIB), this study aims to assess the individuals' behavioural readiness to use for public transportation to improve sustainable mobility instead of using single-occupancy vehicles in Terengganu, Malaysia. This quantitative survey is distributed on 200 citizens at four densely-populated locations in cities of Terengganu, namely Kuala Terengganu, Chukai, Dungun and Jerteh. Data analysis is analysed using structural equation modelling of partial least squares (PLS-SEM). The empirical results indicate that social norm and environmental concern are the main predictor that affecting the behavioural readiness on adopting public transport among the respondents. However, perceived value and affective construct do not have significant impact on citizens’ readiness to use public transport. Policy implications like enhancing service quality, promoting environmental benefits and implementing visible safety measures are addressed. As cities continue to invest in and prioritize public transport, they contribute to a more sustainable and resilient transportation landscape.

Routing of multi-modal autonomous vehicles for system optimal flows and average travel cost equilibrium over time

In the emerging era of autonomous vehicles (AVs), an important question is how to integrate AVs in a multi-modal framework. Recent studies on Transportation Management Centres (TMCs) for future AVs addressed the issue of providing system optimal (SO) solution. However, none of them considered the possibility of developing a multi-modal SO-based solution. In this regard, we propose a novel multi-modal Transportation Management Centre (MMTMC) for future AVs, which controls both the routing and mode use of each traveller and distributes the AVs in such a way that SO flows are maintained for the whole network every day, while travellers on the same origin-destination (OD) have the same Multi-modal Average Travel Time (MMAVT) over a cycle or period of time (in days). That is, travellers on the same OD may encounter different travel times or costs on different days, but over the planning cycle of many days, their travel times will all average to be the same MMAVT, hence preserving the MMAVT equilibrium. Travellers will travel on single occupancy vehicles (SOVs) or high occupancy vehicles (HOVs) on different routes for different days within the planning cycle, while maintaining the same MMAVT overall for the cycle. Travellers on SOVs will experience no crowdedness while on HOVs will experience crowdedness. We model both the cases of minimum system time and minimum system cost (including crowdedness cost), and provide link-based solution first for both cases. For minimum system time, we apply the conventional Frank Wolfe algorithm to generate feasible link flows, whereas for the minimum system cost problem, we incorporate a sensitivity-based equilibrium procedure to derive the SOV, HOV link flows and demand profiles. Afterwards, we utilize the most likely path method to compute unique SOV and HOV path flows and solve the MMAVT equilibrium. Then, we investigate the case of mixed-equilibrium (ME), where some travellers are given the option not to subscribe to the MMTMC and do their own private routing, i.e. user equilibrium (UE) travellers, who are penalized by a toll. The tolls imposed on these MMTMC non-subscribers are defined by the travel time/cost difference between the MMAVT of subscribers and UE users based on their value-of-time (VOT) distribution. This study investigates the properties of this MMTMC routing pattern, the relationship between MMAVT, tolling, and SOV and HOV market penetration, which offers insights to integrate AVs in a multi-modal framework.

Interval-valued Pythagorean fuzzy operational competitiveness rating model for assessing the metaverse integration options of sharing economy in transportation sector

The rise of the sharing economy (SE) has enabled new forms of urban mobility and had a substantial impact on the transportation sector. The applications of SE transportation lessen vehicle ownership and single-occupancy vehicles, which contributes to the sustainability of a region. Metaverse is a digital ecosystem that integrates SE applications with transportation sector and improves the sustainability of SE applications. The aim of this work is to assess and prioritize the Metaverse integration options of sharing economy from multiple criteria and uncertainty perspectives. In this regard, the proposed study develops a four stage decision-making method on interval-valued Pythagorean fuzzy (IVPF) setting. First, new score function is proposed to compare the interval-valued Pythagorean fuzzy numbers (IVPFNs) and further used to derive the weights of decision experts in the proposed method. Second, some IVPF interaction aggregation operators are proposed to aggregate the individual’s information in group decision-making. Third, an integrated weighting process is presented to obtain the objective weights of criteria using cross entropy-based formula and subjective weights by relative closeness coefficient-based model with IVPF information. For this purpose, a cross entropy is introduced for interval-valued Pythagorean fuzzy sets. Fourth, a hybrid operational competitiveness ratings assessment (OCRA) model is developed to rank the alternatives with IVPF information. Moreover, a case study of Metaverse integration options of sharing economy in transportation sector is taken to express the practicability and effectiveness of the presented method. Sensitivity analysis with respect to various values of parameter is discussed to reveal the stability of outcomes. It is also compared with some extant models to validate the superiority of developed method.

Traffic Noise Pollution Modelling and Noise Reduction Asphalt Pavement

Abstract: Noise pollution leads to the lack of concentration of people, as a result, they will be finding longer time for completing the work than that which would be done in a quiet environment and in addition they feel more tired in the noisy area. Road traffic is a complete system which wide comprises of varieties of road user, vehicle and environment interact the congestion of road intersections is due to the motorization from and increase in single occupancy vehicle. This study is carried out to understand the noise traffic pattern of Ottapalam city. So proper planning of the road should be done and proper laws should be available. A study area consisting of two locations, a busy urban street of Ottapalam city, with high population density was selected for the study to determine the impact of the noise pollution at residential zone.

Segmenting the target audience for transportation demand management programs: An investigation between mode shift and individual characteristics

Understanding the characteristics of travelers and situations that are more likely to switch from single occupancy vehicles (SOV) to more sustainable mobility options can help transportation agencies develop better behavior change strategies. Existing mode shift programs, however, rarely identify their target audience, which could make the programs less effective. To investigate the demographics and trip characteristics of those who are more likely to shift from SOV to carpooling, public transit, and micromobility (e.g. walking, biking), a matrix decomposition audience segmentation method is proposed and applied on travel survey data. The results of this research show that for short-distance leisure or social trips, young and middle-income people are more likely to shift to these mobility options from SOV. This study provides a comprehensive and profound understanding of the characteristics of the target audience that can inform policies to create more targeted behavior change strategies to reach their sustainability goals.

Origin-Destination Traffic Survey—Case Study: Data Analyse for Bacau Municipality

In order to develop a transport model for the municipality of Bacau, it was necessary to collect data on the current mobility characteristics of people and goods. Traffic data were collected by means of using an origin-destination (O-D) survey. This survey was carried out in the form of a manual traffic census arranged in stations at six points on the entrance sectors of the national roads in the city of Bacau. At the level of the municipality of Bacau, data from a sample of 3040 drivers were used for the origin-destination surveys. The respondents answered 11 questions. The article presents the results obtained from the six control points for seven questions that were considered relevant. The data obtained were initially used to identify, from a percentage and quantitative point of view, the type of traffic specific to the municipality of Bacau. The analysis of the data shows that the majority of vehicles, 78.9%, originate from Bacau County and that 87.2% of those interviewed have Bacau County as their destination. Most vehicles passing through the checkpoints were within the time intervals 7:30–8:30, i.e., 15.55%, and 16:30–17:30, i.e., 16.18%. The highest proportion of registered vehicles were from Romania, 97.99%. Additionally, 40.75% of the respondents were travelling for business purposes, and approximately the same share was found for the number of people in the vehicle; i.e., single occupant vehicles comprised 58.35% of the total vehicles surveyed. Passenger cars accounted for 67.66% of the vehicles. By using the OriginLab software, the aim was to create parallel graphs for each control point in order to identify certain correlations between the data obtained from the questionnaire. At the end of the article, a hierarchical cluster statistical analysis was carried out by using OriginLab, which identified a series of correlations between the analysed parameters.

Estimating road traffic impacts of commute mode shifts.

This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute travel time is shown to be explainable using only the number of passenger vehicles used for commuting. A universal Bureau of Public Roads model characterizes the sensitivity of each metro area with respect to additional vehicles. The resulting models are then used to determine the change in average travel time for each metro area in scenarios when 25% or 50% of transit and carpool users switch to single occupancy vehicles. Under a 25% mode shift, areas such as San Francisco and New York that are already congested and have high transit ridership may experience round trip travel time increases of 12 minutes (New York) to 20 minutes (San Francisco), costing individual commuters $1065 and $1601 annually in lost time. The travel time increases and corresponding costs can be avoided with an increase in working from home. The main contribution of this work is to provide a model to quantify the potential increase in commute travel times under various behavior changes, that can aid policy making for more efficient commuting.

Driver Fatigue Level Prediction

Public transport networks (PTNs) are difficult to use when the user is unfamiliar with the area they are traveling to. This is true for both infrequent users (including visitors) and regular users who need to travel to areas with which they are not acquainted. In these situations, adequate on-trip navigation information can substantially ease the use of public transportation and be the driving factor in motivating travellers to prefer it over other modes of transportation. However, estimating the localization of a user is not trivial, although it is critical for providing relevant information. I assess relevant design issues for a modular cost-efficient user-friendly on-trip Navigation service that uses position sensors. By helping travellers move from single occupancy vehicles to public transportation systems, communities can reduce traffic congestion as well as its environmental impact. Here, I describe our efforts to increase the satisfaction of current public transportation users and help motivate more people to ride. I can help existing riders and encourage new riders by enhancing the usability of public transportation through good transit traveller information systems. The motivation for every location-based information system is: “To assist with the exact information, at right place in real time with personalized setup and location sensitiveness”. LOCATION-BASED services are increasingly important for modern mobile devices such as the Smartphone. An important feature of a modern mobile device is that it can position itself. Not only for use on the device but also for remote applications that require tracking of the device. Furthermore, tracking has to robustly deliver position updates when faced with changing conditions such as delays due to positioning and communication, and changing positioning accuracy. The realized system tracks pedestrian targets equipped with GPS-enabled devices. The face, an important part of the body, conveys a lot of information. When a driver is in a state of fatigue, the facial expressions, e.g., the frequency of blinking, are different from those in the normal state. In this project, we propose a system called DriCare, which detects the drivers’ fatigue status, such as blinking, and duration of eye closure, using video images, without equipping their bodies with devices. Owing to the shortcomings of previous algorithms, we introduce a new face-tracking algorithm to improve the tracking accuracy. Further, we designed a new detection method for facial regions based on 68 key points. Then we use these facial regions to evaluate the drivers’ state. By combining the features of the eyes and mouth, DriCare can alert the driver using a fatigue warning.

Guaranteed Ride Home Programs: A Study of Program Characteristics, Utilization, and Cost

Guaranteed ride home programs play an important role in increasing public transportation ridership by providing a good reason to separate commuters from their single occupancy vehicles. Since they are inexpensive insurance policies, these programs provide a cost-effective way for a commuter to leave work in the event of a personal or family emergency or unexpected overtime. Research results show that usage rates and costs are quite low. As a possible incentive for commuters to leave their cars at home, these findings may make these programs attractive to communities that want to reduce congestion by increasing public transportation use. The research includes an examination of 55 such programs that serve the top 150 transit agencies in the United States.

Qualitative Research to Assess Interest in Public Transportation for Work Commute

Given the need for reducing single occupancy vehicle commutes, this article presents a case study of employer-based research. Using conjoint analysis as a qualitative research method, factors that potentially influence people’s choices to drive alone to work were studied at a major company in Columbus, Ohio. Such factors included reasons for driving alone, satisfaction with commute, perceptions toward transportation modes, importance of transportation attributes, and likelihood to switch if certain Transportation Demand Management measures were implemented. Target groups were formed by using simple regression and cluster analysis of a stated-ranking question regarding transportation attributes.

How likely will a mode shift occur? – Evaluating the demographic and location differences between potential and existing users of sustainable transportation modes

Transportation mode shift programs do not target specific individuals. Therefore, they are less effective in terms of facilitating mode shifts among existing single-occupancy vehicle (SOV) users. Through a widespread survey-based approach, this proposal compared the demographic and location characteristics of those whose best and second-best mobility option is public transit, micromobility, and carpool and evaluate how likely existing SOV users will shift to the more sustainable mobility options. The demographics and location patterns of the carpool nudgeable audience were found to be substantially different from existing carpool users, implying a lower likelihood for them to shift mode. The results can be used by state and regional agencies with sustainability goals to develop more targeted and effective mode shift programs.

Application of crowdsourced data to infer user satisfaction with Mobility as a Service (MaaS)

Mobility as a Service (MaaS) is a user-centric concept that integrates transportation services, and offers a multimodal journey planner, and online booking and payment options into a single digital platform. Achieving the goal of MaaS, which is to promote sustainable forms of transportation and reduce single occupancy vehicle travel, depends on the needs of different user groups. A review of literature confirms that little remains known about gender-based perspectives towards MaaS. This study aims to identify the features that impact satisfaction across gender. After collecting over 29,000 online reviews, series of text mining and unsupervised machine learning technique were performed to extract features discussed by users in the reviews. Next, ordinal logit regression was used to examine associations between extracted features and level of satisfaction. Findings show that public transit is still the backbone of MaaS, as such, other modes of transportation such as micromobility and ridesharing still receive little attention from users. Although, integrated payment options and booking systems were meant to offer MaaS an advantage over classic journey planners, still fare related features were found to contribute to user dissatisfaction. App related features (such as map, technical issues, and updates), often neglected in transportation studies, showed significant association with satisfaction. One of the main contributions of this study is the expanded understanding of gendered attitudes towards MaaS. Few studies have explored gender perspectives towards new mobility concepts, and particularly MaaS. Increased understanding of gendered perspectives encourages the use of MaaS, and ultimately enhances travel experiences for all users.

Factors That Influence Travelers’ Willingness to Adopt Bus Rapid Transit (Green Line) Service in Karachi

Bus rapid transit (BRT) system is a sustainable mode choice alternative and traffic management method for traffic congestion problems in urban areas. As an extent of total demand management, BRT has broadly been implemented in many countries. BRT has proven to be progressive in alleviating traffic congestion and the difficulty of finding parking spaces in city centers. Currently, people driving their automobiles to work cause traffic congestion along Karachi’s main corridors. People cannot be persuaded to use public transit until their travel patterns are understood. Therefore, the disparity between public and private transportation must be addressed. This research aimed to develop a model to shift car travelers toward Karachi’s Green Line BRT and investigate the factors that influence car travelers’ decisions. A questionnaire-based survey was carried out on single-occupant vehicle (SOV) users in the Green Line corridor of Karachi. This study investigated the elements that influence SOV users’ willingness to adopt the BRT system and studied the possible ways of attracting car drivers to BRT. Data were examined using descriptive-analytic techniques such as the contingency table approach in conjunction with a Chi-square test of the independence/association model in SPSS. Furthermore, binary logistic regression was applied to the highly mediated associated variables. The research’s outcomes were geared at the imposition of parking fees at workplaces to deter individuals from parking their automobiles there. SOV travelers can be diverted to BRT services using this strategy. The research findings will assist policymakers and serve as a foundation for scientific investigations on the travel demand model for the BRT system.

Assessing Spatial Variations of Traffic Congestion Using Traffic Index Data in a Developing City: Lessons from Johannesburg, South Africa

The COVID-19 pandemic has created unforeseen effects in public transport and the mobility of people in cities globally. Johannesburg, being a developing city in one of the most affected countries in Africa during the pandemic, has experienced severe changes in traffic management and travel patterns as a result of the restrictions imposed on movement. Hence, this study examined the spatial variations in traffic during the pandemic. The study utilized data obtained from the TomTom Traffic Index for the city of Johannesburg from 2019 to 2021, with 2019 representing the period pre-COVID-19 with no lockdown restrictions, 2020 representing the period with restricted movement to limit spread of COVID-19, and 2021 representing a period of relaxed and minimized restrictions on movement. Our findings revealed that there was a variation in congestion levels between 2019–2021 with year 2020 having the least congestion from the beginning of the COVID-19 restrictions due to regulations enforced in movement and reduced travel. Our findings further revealed that traffic congestion was higher during weekdays than weekends during the three periods, with mini-bus taxis as the major contributors to congestion. Consequently, there is a need to discourage the use of single occupancy vehicles and invest in more sustainable means of transportation to ease the mobility of people and reduce traffic on major roads.

Extreme Heat Impacts on the Viability of Alternative Transportation for Reducing Ozone Pollution: A Case Study from Maricopa County, Arizona

Abstract One commonly proposed strategy for reducing urban air pollution is transitioning from single-occupancy vehicle (SOV) travel to alternative transportation (AT) modes, such as walking, biking, and using public transportation. While many studies have addressed the benefits of switching from SOV to AT, fewer studies have examined the potential for negative outcomes due to increased exposure to heat when using AT modes. This work uses Maricopa County, Arizona, home to the metropolitan Phoenix area, as a test case to examine the potential impacts of heat on commuters who utilize AT. First, regions of the county with the most candidates for switching from SOV to AT were identified and used to develop an AT candidate index. This index was based on both the current rates of AT use and the number of SOV commuters with the shortest commuting times in the dataset (<10 min). Next, typical weather conditions during warnings for high ozone (O3) pollution were examined. From 2017 to 2020, over one-quarter of all days with an O3 warning also were subject to an excessive heat warning. Last, land surface temperature data were used to determine the potential for increased heat exposure during AT commuting at both the ZIP code and AT infrastructure (public transit stops and bikeways) scales. Although this work focuses on Maricopa County, the issues presented here are increasingly relevant for cities across the world that are subject to poor air quality, hotter temperatures, and heat waves.

QS ride app

The quickly increasing number of vehicles associated commuter’s results in a rise in holdup and also the issues related to it. It needs the study of different measures to scale down the amount of vehicles movement daily, particularly single-occupant vehicles. Many researches conducted discovered that carpooling are often an economical resolution to alleviate the pressure thanks to traffic. The carpooling may be a system by that an individual offers his or her personal vehicle to at least one or additional commuters who have similar destinations or routes. Carpooling is effective solution to cut back the traffic. It consists of accelerating the occupancy of vehicles by occupying empty seats within the vehicle effectively. This system isn't enough as so much as tiny regions are thought-about and it's quantifiability issue. Our system will offer a secure interface for connecting drivers and commuters to share the ride whereby we have a tendency to are reaching to provide this service in small regions also in order that daily commuters like office-goers and students may also be expedited and might contribute in reducing traffic so saving environment.

Effectiveness of Free Bikes and E-Bikes for Commute Mode Shift: The Case of Google’s Lending Program

In 2015, Google began a new transportation demand management program designed to increase bike commuting to their two main corporate campuses in Mountain View and Sunnyvale, CA, United States by lending conventional and electric assisted bikes to employees at no cost to them. Following the lending period, Google incentivized bike purchases, among many other program co-benefits to increase bike commuting. Using a series of bivariate and multivariable analyses, we estimate the program led to average bike commute increases of approximately 1.7–2.3 days per week, roughly a tripling of prior bike commute rates for participating employees. After the program, bike rates of participants diminished slightly, but were still greater than baseline (increase of 1.3–1.9 days per week). Furthermore, nearly all the increases in bicycling are likely attributed to decreases in single occupancy vehicle (SOV) commuting. This study offers a first look at the potential for bike lending as a transportation demand management strategy for large employers in suburban settings which can help other employers design their own programs.