Improve Travel Time Research Articles

Autonomous Vehicles and Urban Traffic Management for Sustainability: Impacts of Transition of Control and Dedicated Lanes

Autonomous vehicles (AVs) are increasingly recognized for their potential to enhance urban traffic systems, particularly in traffic management and sustainability. This study explores AV integration into urban networks, focusing on transitions of control (ToC) and dedicated lane (DL) applications at varying AV penetration rates. Through simulations, various scenarios reveal the complex interactions between AVs and human-driven vehicles in mixed traffic conditions. The findings show that DLs can reduce local density, occupancy, and time loss by 5–35%, while improving travel time reliability by 15–25%. On an urban scale, DLs generally enhance traffic flow and reduce emissions, though the effects of ToC vary based on traffic conditions and AV automation levels. At lower AV penetration rates, ToC can lead to increased travel times and up to a 10% decline in traffic performance due to unpredictable human driver behavior during control transitions. The results highlight that DLs can significantly improve traffic flow, travel time reliability, and emissions, thereby contributing to sustainable urban mobility. However, the impacts of ToC are more complex, depending on specific traffic conditions and AV automation levels. This study emphasizes the importance of well-designed ToC and DL applications to optimize AV integration and support a balanced, sustainable future for urban mobility.

Energy harvesting for automated storage and retrieval system with sustainable configuration of storage assignment and input/output point

The warehouse automation market has experienced significant growth due to the necessity for quick responses to customer needs. The adoption of Automated Storage and Retrieval System (AS/RS) aims to enhance operational efficiency and expedite order fulfillment, although environmental considerations are frequently overlooked. This study introduces the implementation of energy harvesting using Regenerative Braking System (RBS) on AS/RS to minimize the carbon emission impact. The best configuration of storage assignments and Input/Output (I/O) points is examined to improve travel time, response time, and carbon emission as sustainability indicators. This study employs a discrete-event simulation mimicking the AS/RS and warehouse environment under uncertainty. Simulation-based experiment was performed under 96 different scenarios and the result was assessed through statistical tests revealing the main and interaction effects between factors to performance indicators, including the trade-off between them. The result reveals that the implementation of RBS in AS/RS can result in 13% energy saving on average or equal to additional travel range of 28,800 m indicating the suitability adoption towards green operation. However, the lowest carbon emission is followed by higher travel time and response time. Thus, metamodel-based optimization was also performed via desirability function analysis. The optimization result reveals that the sustainable AS/RS configuration is obtained with a single-side for I/O point, non-class for storage classification, closest open location with column-order for slot selection, and closest open location with row-order for retrieval selection.

Transit fares integrating alternative modes as a delay insurance

Public transport (PT) fare policy remains subject to innovations, not least evident in the Mobility as a Service discussion. Mode integration and related fare strategies can be used to increase the attractiveness of PT by compensating for potential delays. This study proposes “premium fares” as a novel pricing tool that can evaluate and improve the travel time reliability on a multimodal transportation network. The premium fare is higher than the standard fare but allows passengers to use an alternative service free of charge if waiting for the delayed public transport service is anticipated to be longer than a certain qualification threshold. Properties that guarantee monotonicity of the premium fare with respect to distance traveled are developed. The operator aims to find the premium fare price and qualification threshold that can maximize its profit, based on the probability distributions of delay and passengers’ value of time (VOT). We model this optimization problem for a railway line given a limited capacity of alternative mode services, e.g., the number of taxis. A two-stage approach using nonlinear and dynamic programming is developed to obtain the optimal decision variables and associated capacity allocation plan. Our results show that the introduction of the premium fare can benefit both operators and travelers with increased profits and improved travel time reliability. The interplay between fares and the qualification threshold is illustrated using various delay and VOT distributions. To attract enough customers the premium fare has to be set below a specific level dependent on the VOT distribution. Meanwhile, the operator adjusts the qualification threshold to control the cost paid to the alternative service provider.

Exploring the determinants of autonomous minibus adoption: empirical findings from a demand-based service in Germany

Autonomous on-demand services as part of public transport are discussed to improve public transport substantially. A household survey in Karlsruhe, Germany, was conducted among inhabitants of a residential area where a combined autonomous and on-demand minibus service with automation level 4 was offered. The study investigates the residents’ appraisal of this service and reasons for using and not using it. Results indicate that people generally have a positive attitude towards it and are willing to use it in the future. Difficulties are found in travel speed, availability, and complexity of using such a new service. Favorable factors in the intention to use the service are having a mobility impairment, being open to other forms of new mobility, and not having a car in the household. In the future, to be successful, such services should improve travel times and reliability and address issues of their primary target group, such as the high complexity of accessing these services.

Safety Analysis of 6-Lane National Highway Using Reactive and Proactive Methods: A Case Study

Abstract Globally, traffic-related deaths were 1.35 million per year or nearly 3,700 per day. However, the severity of accidents Over a decade increased by 31.5%, with 37.3 people dying per 100 accidents in India. 4,12,432 accidents (approximately 422 people die daily) were reported in 2021, with 1,53,972 fatalities on Indian highways. National Highways’ safety performance is vital in developing countries such as India. This research paper focuses on the six-lane Jaipur Ring Road, an approximate 150-kilometer-long national highway surrounding the city of Jaipur, India. It is a crucial infrastructure project aimed at easing traffic congestion in and around Jaipur. Initiated in the early 2000s and completed in 2020, the road connects major highways and national highways, reducing traffic congestion and improving connectivity in and around the city. This paper explores the benefits of the Jaipur Ring Road, including improved travel time for commuters, transportation of goods, reduced accident causes, and economic development. Moreover, the article emphasizes the significance of monitoring road conditions to guarantee a secure, effective, and adaptive transportation system. This involves gathering information on road performance safety conditions and identifying areas that require enhancements. The paper highlights the role of trained professionals and community stakeholders in conducting road observations and ensuring that transportation infrastructure meets the needs of local communities. This case study presents some crucial observations made at specific locations on the Jaipur Ring Road. The study aims to analyze the traffic flow, identify potential bottlenecks, and propose solutions to improve the road’s efficiency and safety.

Modeling travel mode choice behavior for planned special events using CPT-based HI-MADM approach

ABSTRACT Planned special events (PSEs) can significantly increase traffic demand, posing greater challenges than daily operations. In response, host cities often invest heavily in transportation upgrades. This study proposes a model integrating cumulative prospect theory (CPT) and multiple-attribute decision-making to understand mode choice behavior of PSE attendees. This integration enables direct extraction of traveler preference information, offering a more suitable description of the decision-making process. The decision matrix considers mode and attribute heterogeneity. The reference point for CPT is derived from previous PSE trips or commutes. Furthermore, this paper assesses asymmetric nonlinear sensitivity and distorted perceptions of different attributes. Analysis of mode-choice behavior for PSE participation, using survey data from Tianjin, China, validates the approach. It better characterizes PSE choice behaviors and demonstrates varying sensitivity to gains and losses, dependent on different attributes and distances. Thus, event planners should prioritize improving travel time and convenience to increase public transport mode share.

Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks

AbstractMultimodal freight transport allows switching among various modes of transportation to efficiently utilize transport facilities. A multimodal transport system incorporates geographical scales from global to local. Travel time estimation in a multi‐modal cargo transportation network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel time prediction is of great importance for cargo transportation, as it enables SC participants to increase logistics efficiency and quality. It requires adequate input data, which can be generated. In recent times, the machine learning (ML) algorithm has been well‐suited to resolve complex and nonlinear relationships in the collected tracking data. This study designs a deep learning‐powered travel time estimation in multimodal freight transportation networks (DLTTE‐MFTN) technique. The goal of the DLTTE‐MFTN technique is to estimate the travel time using a hyperparameter‐tuned ensemble learning approach. To achieve this, the DLTTE‐MFTN method initially undergoes data pre‐processing to convert the input raw data into a useful format. In addition, the singular value decomposition (SVD) model can be applied for feature dimensionality reduction in multimodal transport data, considerably improving travel time prediction. Besides, the DLTTE‐MFTN method estimates travel time using an ensemble of three DL approaches including one‐dimensional convolutional neural network (1D‐CNN), stacked autoencoder (SAE) attention, and recurrent neural network (RNN). Finally, the hyperparameter tuning of the DL models takes place using the whale optimization algorithm (WOA). The performance analysis of the DLTTE‐MFTN method takes place using the Kaggle dataset. The experimental results stated that the DLTTE‐MFTN technique attains superior performance over other ML and DL models.

The impact of congestion and dedicated lanes on on-demand multimodal transit systems

Traffic congestion can have a detrimental effect on public transit systems, and understanding and mitigating these effects is of critical importance for effective public transportation. Implementing Dedicated Bus Lanes (DBLs) is a well-known intervention to achieve this goal. A DBL is a designated lane for bus transit, which avoids congestion and substantially lowers the travel time. This makes transit more attractive, encouraging more travelers to adopt public transportation. This paper studies the impact of congestion and DBLs on novel On-Demand Multimodal Transit Systems (ODMTS). ODMTS combine traditional rail and bus networks with on-demand shuttles. Previous case studies have shown that ODMTS may simultaneously improve travel time, reduce system cost, and attract new passengers. Those benefits were shown for an ideal world without traffic congestion, and this paper hypothesizes that the advantages of ODMTS can be even more pronounced in the real world. This paper explores this hypothesis by creating realistic congestion scenarios and solving bilevel optimization problems to design ODMTS under these scenarios. The impact of DBLs on ODMTS is evaluated with a comprehensive case study in the Metro Atlanta Area. The results show that DBLs can significantly improve travel times and are effective at increasing adoption of the system.

Mobility justice or transit boosterism? The use of rail transit as an urban transformation strategy in Kitchener, Canada, and Malmö, Sweden

Many cities and countries have embraced transit-oriented development as an international growth management approach, shifting trips from car-oriented to transit. But in this race to become more sustainable, who is being left out? Using mobility justice as a theoretical framework, this paper presents a qualitative comparison between Malmö, Sweden, and Kitchener, Canada, two mid-sized cities where new rail-based infrastructure was completed in 2019. Using over 40 interviews with local residents, business owners and staff, planners, and private sector developers in each city, we found that transportation improvements have created unequal transport mobilities. In both cities, new transportation infrastructure did not improve travel times or access to transit for existing users; in Kitchener, local changes to make way for the new LRT had negative effects on the neighborhood, and bus transit times increased, while in Malmö bus services remained the same as the new train was barely used. This detachment of transportation needs from infrastructure is necessary for local and state politicians to promote new infrastructure as a branding approach. We call this transit boosterism.

Operation standards for exclusive bus lane on expressway using simulation and traffic big data

<abstract> <p>Korea operates exclusive bus lanes (XBLs) on many of its expressways. As XBLs convert one lane of regular-use traffic into bus-only traffic, they have a large impact on traffic flow, so careful judgment is required to determine if the operation is effective. However, XBLs have been operated based on only political judgment due to the lack of standards for the operation of expressway XBLs. Therefore, we sought to establish standards for the operation of expressway XBLs using the micro-traffic simulation program VISSIM and the multi-criteria value function methodology. Various scenarios were established based on traffic volume changes of passenger vehicles and bus traffic of four- and five-lane expressway networks. Through an expert survey, the average speed and speed-deviation values, which are the criteria for evaluating operational efficiency and safety, were determined. Also, value points were converted using average speed and speed deviation extracted from the simulation. In addition, quantitative operation standards were established using the converted value scores. Using the results of this study, we established standards for the operation of the XBLs and presented guidelines for related agencies such as police, bus groups, and corporations. The National Police have prepared guidelines for the operation of the XBLs. Citing the results of this study, the new guidelines were implemented in February 2021, and sections of some XBLs have been abolished. Through this study, quantitative standards for the operation of XBLs, one of the management lane techniques necessary for sustainable highway operation, were prepared and applied to actual highways. By properly applying the newly applied guidelines according to quantitative standards, there will be effects of reducing traffic congestion, improving travel time, and enhancing environmental characteristics such as exhaust gas emission. It is also expected to have a positive effect on safety.</p> </abstract>

Avaliação da resiliência e da vulnerabilidade da mobilidade no contexto de políticas desafiadoras de tarifas de transporte: um estudo de caso do Rio de Janeiro

Abstract Resilience of urban mobility is an emerging topic, with most studies focusing on natural disasters or technical disturbances. However, there is a gap in understanding the resilience and vulnerability of urban mobility in the face of economic threats and inadequate transport policies, particularly in developing countries such as Brazil, characterized by social inequality and urban segregation. This study sheds light on the issue of urban inequalities and segregation, aiming to identify the most vulnerable and resilient areas of Rio de Janeiro in the event of an economic crisis. We assessed the resilience and vulnerability of the public transport system with commonly used accessibility indicators, applying fuzzy logic to data from 160 districts. We considered a scenario without the fare subsidy program. The results indicate that districts with better access to high-capacity transport systems and job opportunities exhibit higher levels of resilience and lower vulnerability. However, the study also uncovers socio-spatial inequalities, with resilience values tending to be higher in coastal areas and central business districts, exacerbating disparities. Addressing urban inequalities and segregation requires not only improving travel times and transportation systems but also considering the economic impact on vulnerable populations and promoting decentralized employment opportunities.

Simulating the performance of HOV lanes for optimal urban traffic management

Traffic congestion is a significant problem in crowded cities, resulting in increased travel times, air pollution, and reduced productivity. High-occupancy vehicle (HOV) lanes have emerged as a promising solution to tackle these issues. HOV lanes are reserved for vehicles carrying multiple passengers, incentivizing shared travel and reducing the number of low-occupancy vehicles on the road like school buses. By reducing the overall number of cars on the road, HOV lanes can significantly alleviate traffic congestion, improve travel times, and reduce air pollution. The effectiveness of HOV lanes depends on the degree of demand for shared travel and the availability of incentives, such as employer-provided benefits or toll exemptions. Additionally, the design and implementation of HOV lanes must be carefully planned to ensure that they do not negatively impact other road users. Tehran, as one of the largest and most populous cities in Asia, is a major destination for daily travel by workers. The Tehran-Karaj freeway serves as the primary and busiest entrance to Tehran city, experiencing heavy traffic every day in the morning towards Tehran and in the evening towards Karaj. In this study, the traffic conditions of this freeway were simulated in the existing state and with the presence of HOV lanes, and the resulting traffic parameters were compared and analyzed under different traffic scenarios. The findings of this research can offer valuable insights for decision-makers and traffic policy makers.

Trajectory planning at a signalized road section in a mixed traffic environment considering lane-changing of CAVs and stochasticity of HDVs

Connected and automated vehicles (CAVs) are projected to bring significant benefits to traffic efficiency and driving comfort. However, the realization of full CAV penetration rate will take a long time. In this paper, a framework is proposed for planning the trajectories of vehicles at a signalized road section in a mixed traffic environment consisting of CAVs, human-driven vehicles (HDVs) and connected and automated buses (CABs). In the proposed trajectory planning framework (TPF), both the lane-changing (LC) behavior of CAVs and the stochasticity of HDVs are considered. The whole TPF is composed of a planning module, a running module, and a switching module. In the planning module, the mixed integer programming (MIP) models for trajectory planning with/without LCs are formulated to optimize the trajectories of CAVs/CABs. A parsimonious algorithm is designed to determine a suitable planning time horizon for the MIP models. The running module and the switching module are designed to ensure the driving safety of vehicles. To consider the stochasticity of HDVs, the concept of α-trajectory is employed in simulations to produce predicted trajectories of HDVs, while the actual trajectories of HDVs are generated by a stochastic car-following model. Here, α is a parameter between 0 and 1 related to one randomly changing parameter of HDVs, and α-trajectory is a series of predicted trajectories generated for HDVs based on the value of α. A rolling time horizon scheme is applied for TPF to account for the time-varying traffic situations. Numerical experiments under different traffic states and market penetration rates (MPRs) of CAVs/CABs are conducted to validate the performance of the proposed TPF. The average improvement in travel time and fuel consumption can reach up to 28.9 %, 17.8 % and 52.2 %, 35.3 % under medium and heavy traffic, respectively, and the average improvement in driving comfort is over 20 % in most traffic scenarios. The comparison experiment without fixed rolling time window (FTW) shows the advantage of setting FTW in adapting to the time-varying traffic situations, especially under heavy traffic. The sensitivity analysis shows that the values of α1 and α2 associated with α-trajectory can affect the performance of the proposed TPF in varying degrees. Finally, the length of the control zone is suggested to be set to 300–500 m.

Impact of connected and automated vehicles on the travel time reliability of an urban network

Connected and automated vehicles (CAVs) have the potential to revolutionise the transportation industry, with a plethora of research already revealing considerable gains in safety, travel time and mobility, as well as reduced congestion and pollution. As the number of CAVs on the road grows, rigorous testing for various market penetration rates (MPRs) of CAVS is essential to determine under what conditions the benefits can be realised. For the studies investigating the impact of CAVs on travel time reliability specifically, the MPRs in which the network most thrives have been inconsistent. The majority of the research is concerned with highway networks with only a few travel time reliability studies that focus on urban networks. In this simulation study, the impact of varying MPRs of CAVs on travel time reliability is evaluated in an urban network for different traffic demands. Travel time reliability metrics are assessed, including the standard deviation, buffer time index and misery index. The study demonstrated that from 0% to 100% MPR, the overall weighted average travel time decreased by 28%, and the standard deviation of the weighted average travel time declined by 35%, highlighting the significant increase in travel time reliability. Travel time improvements were visible from the MPR of 10%; however, the reliability metrics highlighted the greatest benefits occurred at higher MPRs. This study presents valuable results about the reliability that CAVs can bring to urban networks during the fleet transition to CAVs.

Exploring the Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries

Exploring the Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries

Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time.

TRAFFIC SPEED MODELLING TO IMPROVE TRAVEL TIME ESTIMATION IN OPENROUTESERVICE

Abstract. Time-dependent traffic speed information at a street level is important for routing services to estimate accurate travel times and to recommend routes which avoid traffic congestion. Still, most open-source routing machines that use OpenStreetMap (OSM) as the primary data source rely on static driving speeds derived from OSM tags, since comprehensive traffic speed data is not openly available. In this study, a method was developed to model traffic speed by hour of day at a street level using open data from OpenStreetMap, Twitter and population data. The modelled traffic speed data was subsequently integrated into the open-source routing engine openrouteservice to improve travel time estimation in route planning. Machine learning models were trained for ten cities worldwide using traffic speed data from Uber Movement as reference data. Different indicators based on geolocation and timestamp of Twitter data as well as a geographically adapted betweeness centrality indicator were evaluated for their potential to improve prediction accuracy. In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The centrality indicator improved the model as well but to a lesser extent. The Google Routing API was used as reference to evaluate the accuracy in travel time estimation. Deviations in travel times were regionally different and were partly alleviated by including the raw traffic data by Uber or the modelled traffic speed data in openrouteservice.

Vehicular Visible Light Communication for Intersection Management

An innovative treatment for congested urban road networks is the split intersection. Here, a congested two-way–two-way traffic light-controlled intersection is transformed into two lighter intersections. By reducing conflict points and improving travel time, it facilitates smoother flow with less driver delay. We propose a visible light communication system based on Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communications able to safely manage vehicles crossing through an intersection, leveraging Edge of Things (EoT) facilities. Headlights, street lamps, and traffic signals are used by connected vehicles to communicate with one another and with infrastructure. Through internally installed Driver Agents, an Intersection Manager coordinates traffic flow and interacts with vehicles. For the safe passage of vehicles across intersections, request/response mechanisms and time and space relative pose concepts are used. A virtual scenario is proposed, and a “mesh/cellular” hybrid architecture used. Light signals are emitted by transmitters by encoding, modulating, and converting data. Optical sensors with light-filtering properties are used as receivers and decoders. The VLC request/response concept uplink and downlink communication between the infrastructure and the vehicles is tested. Based on the results, the short-range mesh network provides a secure communication path between street lamp controllers and edge computers through neighbor traffic light controllers that have active cellular connections, as well as peer-to-peer communication, allowing V-VLC ready cars to exchange information.

Bus priority lane in Bengaluru: A study on its effectiveness and driver stress

This paper studies the effectiveness of the bus priority lane (BPL) for public transport buses in the city of Bengaluru in India. We use the travel times on the BPL corridor as a measure of the effectiveness of the BPL. We find that there is a significant improvement in the travel times after the introduction of the BPL; for the worst 10% of the travel times, we find an improvement between 4% and 28%. Our methodology involves extracting trips on the BPL and computing the travel times for these trips from a time series of GPS information. Our methodology is scalable and can be used to compute the travel times between any two given points in other similar studies. We supplement our results with a novel test (called the D-test) for comparing the levels of stressful driving in the following scenarios: (a) morning peak hours (IST 07:00 h to 11:00 h) versus evening peak hours (IST 17:00 h to 21:00 h), and (b) northward trips versus southward trips on the BPL. We are able to infer that the drivers are generally more stressed during the morning peak hours and during the southward trips on the BPL. Partitioning the BPL into segments, we show that a majority of the segments exhibit similar effectiveness and driver stress trends as the full BPL stretch. We anticipate that corrective measures for the betterment of travel times and driver stress levels (e.g., introducing additional buses subject to vehicle re-balancing constraints, carefully planning the bus schedules to regulate bus traffic throughout the day, etc.) in some segments can lead to further improvements in travel times and reduction in driver stress levels.

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy.

In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway's throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power.