Travel Delay Research Articles

An Investigation of Travel Distance and Timeliness of Breast Cancer Treatment Among a Diverse Cohort in the United States

Travel to and from distant cancer treatment facilities can place a significant burden on cancer patients, particularly rural and minority survivors. Thus, the purpose of our investigation was to examine the association between patient travel distance and delays in types of treatment for breast cancer (surgery, radiation, chemotherapy, and hormonal therapy) and treatment delays. Using a novel linkage between the state cancer registry and administrative data from Medicaid and a private insurance plan, 2155 BC patients were successfully linked to create the cohort. ArcGIS was used to geocode all case residences and treatment facility addresses and calculate network distance between the residence and each facility. Logistic regression models were used to calculate the adjusted odds of being delayed versus timely by street distance. Odds of late surgery were increased by 1% (95% CI: 1.00, 1.01) for each one-mile increase from the patient’s residence to the treatment facility. In race-stratified models, the odds of late treatment for Black patients increased by 3% per mile (95% CI 1.01, 1.06) for radiation. Increased travel distance appears to significantly increase treatment delays for surgical, radiation, and chemotherapeutic treatments for women with BC, especially among Black women.

Assessing the Impacts of Queue Jump Lanes at a Signalized Intersection: A Case Study of Narayan Gopal Intersection

One of the main causes of congestion in Nepal's urban areas is the preference for private transportation over public transportation. In other contexts, research on bus priority measures has demonstrated their potential to reduce transit delays, enhance bus reliability in mixed-traffic conditions, and optimize travel times. This study aims to assess the effectiveness of introducing queue jump lanes in an isolated signalized intersection of Kathmandu Valley, applying these concepts to Nepal's heterogeneous traffic conditions. VISSIM microsimulation software was used to model the existing scenario and four different QJL configurations at the Maharajgunj intersection to assess the impacts on travel time and delay. Results reveal that adding a left-turn-only lane adjacent to a QJL with pre-signal can significantly save travel time and reduce delay at the Maharajgunj intersection.

Reinforcement learning‐based approach for urban road project scheduling considering alternative closure types

AbstractGrowth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re‐reroute traffic or implement partial closure. Both options have significant implications for peri‐construction road capacity, traveler costs, and the project duration and cost. This study presents a decision‐making methodology to facilitate the choice between full road closure and partial closure. The presented decision‐making methodology is a bi‐level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower‐level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path‐loyal travelers who do not change their routes from their pre‐construction routes. The bi‐level mixed integer nonlinear model is solved using a reinforcement learning‐based algorithm (the multi‐armed bandit‐guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path‐loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.

Improving Highway Work Zone Mobility in the Developing World: A Systematic Literature Review of Work Zone Delay Measures and Technological Solutions

Work zones have long been cited as a significant cause of road fatalities and traffic delays, with evident negative impacts on traffic safety and mobility. This review outlines the mobility measures reported in the work zone delay context in three thrusts: 1) work zone travel delay, 2) queueing length, and 3) user cost. Through a critical comparison of reported findings, the current review illuminates the possibilities of more guided research that would expedite the goal of achieving an optimal work zone layout with minimal fatalities, maximum mobility, and efficiency. Additionally, we expanded our review to include other engineering disciplines to identify potential technologies that could be used to make work zones smarter. Finally, policy interventions necessary to improve the safety and mobility of work zones are discussed, followed by a critical discussion of the way forward.

Travel Time Variability in Urban Mobility: Exploring Transportation System Reliability Performance Using Ridesharing Data

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited.

A dynamic priority bus lane strategy in heterogeneous traffic environments

Allowing connected and automated vehicles (CAVs) to drive in bus lanes can enhance the utilization efficiency of exclusive bus lanes. However, this could also lead to a certain degree of heightened bus operating delays. In a connected and automated environment, vehicle-to-vehicle (V2V) communication is essential for implementing dynamic management strategies for bus lanes. This study focuses on urban arterial roads as the research scenario. Based on the characteristics of the car-following and lane-changing behaviors of human-driven vehicles (HDVs) and CAVs, a simulation platform for heterogeneous traffic flow is established. Four strategies for bus lane control are proposed: exclusive bus lanes (EBLs), bus and CAV mixed lanes (BCMLs), dynamic bus lanes (DBLs), and dynamic priority bus lanes (DPBLs). Through simulation experiments, an analysis is conducted to compare traffic operation characteristics under different bus lane strategies, assess the advantages of these strategies, and determine the traffic density conditions for their implementation. The results indicate that in comparison to the EBL strategy, the other strategies enhance traffic flows but result in different levels of travel delays for buses. Compared to the DBL and BCML strategies, the DPBL strategy is applicable under a broader range of traffic density conditions. Additionally, sensitivity analysis revealed that the effects of the V2V communication distance and bus flows on the applicability of DPBL strategies are relatively minor. These results provide a theoretical basis and practical guidance for future bus lane management.

Assessing Traffic Performance: Comparative Study of Human and Automated HGVs in Urban Intersections and Highway Segments

This study conducts a comparative analysis of traf�ic dynamics at urban signalized intersections and on highways, incorporating both human-operated and automated heavy goods vehicles (HGVs) using the PTV VISSIM simulation model. It examines the impacts of automated driving technologies on critical traf�ic performance metrics such as queue length, travel time, vehicle delay, emissions, and fuel consumption. Initial �indings indicate that automation in HGVs enhances traf�ic �low, particularly by reducing queue lengths and vehicle delays. However, varying levels of automation from cautious to aggressive reveal complex trade-offs between operational ef�iciency and environmental impacts. On highways, automated HGVs demonstrate superior performance by reducing travel times and delays while increasing throughput compared to human-driven HGVs. These results underscore the operational bene�its of automated HGVs under diverse traf�ic conditionsand highlight their signi�icant implications for transportation planning and policy-making. This research contributes valuable insights into the integration of automated technologies in transportation systems, facilitating informed decision-making for stakeholders considering the adoption of these advancements in the current infrastructure.

A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios

Autonomous driving holds significant promise for substantially reducing road fatalities. Unlike traditional machine learning methods that have conventionally been applied to enhance the motion control of Autonomous Vehicles (AVs), recent attention has shifted toward the utilization of Deep Learning (DL) and Deep Reinforcement Learning (DRL) techniques. These advanced approaches have the potential to greatly improve AV vehicle control and empower vehicles to learn from their surroundings. However, the majority of existing research has concentrated on straightforward scenarios, often neglecting the intricate challenges posed by vulnerable road users such as pedestrians, cyclists, and motorcyclists, as well as the influence of varying weather conditions. In this study, we propose a novel model founded on DRL, specifically leveraging Deep-Q Networks (DQN), to effectively manage AVs in complex scenarios characterized by heavy traffic, diverse road users, and diverse weather conditions. Our approach involves training the model in diverse weather conditions, encompassing clear daytime and nighttime as well as challenging weather conditions like heavy rainfall during both the day and sunset. Through this comprehensive training, the AV becomes proficient in navigating safely through intersections and reaching its destination without any accidents. To rigorously evaluate and validate our proposed approach, extensive testing was conducted employing the CARLA simulator. The simulation results unequivocally demonstrate that our model not only reduces travel delays but also minimizes the occurrence of collisions, marking a significant step forward in achieving safer and more efficient autonomous driving.

Multigroup invariance of measure for angry drivers (MAD) scale using a representative sample of drivers in Australia

Introduction: Driver anger and aggression have been linked to crash involvement and injury outcomes. Improved road safety outcomes may be achieved through understanding the causes of driver anger, and interventions designed to reduce this anger or prevent it from becoming aggression. Scales to measure anger propensities will be an important tool in this work. The measure for angry drivers (MAD; Stephens et al., 2019) is a contemporary scale designed to measure tendencies for anger across three types of driving scenarios: perceived danger from others, travel delays, and hostility or aggression from other drivers. Method: This study aimed to validate MAD using a representative sample of Australian drivers, stratified across age, gender, and location. Participants completed a 10-minute online survey that included MAD, sought demographic information (age, gender, driving purpose, crash history), as well as the frequency of aggressive driving. Multigroup confirmatory factor analyses (MGCFA) assessed how stable the structure of the MAD was across drivers of different ages, gender, purposes for driving and those who do or do not display anger aggressively. MAD was invariant across all groups, showing that all drivers interpreted and responded to MAD in the same way. Results: A comparison of latent means showed anger tendencies were higher for men compared to women, for younger drivers compared to older drivers, and for those who drive mainly for work compared to those who mainly drive for other reasons. When controlling for driver factors, driving anger was associated with increased odds of being aggressive while driving. Practical Applications: Overall, this study demonstrated that MAD is an appropriate scale to measure anger tendencies and can be used to support interventions, and evaluation of interventions, to reduce anger and aggressive driving.

Enhancing Roadway Efficiency through Comprehensive Studies on Travel Time, Delays, and Spot Speeds

This study integrates travel time and delay with spot speed study, utilizing the floating car method. The correlation between increased traffic flow and elevated delay times is underscored. Notably, a significant portion of vehicles operating below anticipated speeds emerges as a pivotal factor contributing to congestion. The average journey time is approximately 4 minutes, with an average stopped delay ranging from 0.17 to 0.11 minutes. Spot speed study in four directions reveals nuanced speed dynamics. In the East-West direction, the 15th, 85th, and 98th percentile speeds are 39.14 km/h, 62.27 km/h, and 85.00 km/h and similar patterns are observed in the other directions. These results transcend routine traffic studies, offering actionable insights for urban planning, traffic regulation, and economic assessments. The study delves beyond statistical compilations, providing concrete evidence of the intricate interplay between traffic flow, delay times, and speed dynamics. It aspires to guide traffic engineers, urban planners, and decision-makers into the realms of future urban development. The comprehensive understanding derived ensures informed decision-making in transportation management and infrastructure planning.

Framework for Assessing the Sustainability Impacts of Truck Routing Strategies

The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.

Integrated operations strategies for shared and privately-owned autonomous vehicles: A deep reinforcement learning framework

The shared autonomous vehicle (SAV), combining shared mobility and vehicle automation, will play a pivotal role in urban transportation. Effectively matching SAVs to stochastic customer demand poses a significant challenge, particularly when addressing microscopic multi-AV coordination to prevent conflicts and congestion within a network. To tackle this issue, we propose a novel traffic control scheme called the Integrated Operation Framework for Order Matching and AV Coordination (I-OFOMAC). This scheme introduces a third-party coordinator responsible for integrating online order matching for the SAV platform and real-time AV coordination for the road manager. Our approach utilizes an offline deep reinforcement learning method to train a state value neural network, which facilitates strategic order matching. Additionally, a neural network approximating bisimulation metrics is trained to enhance exploration and accelerate learning. To achieve scalability in network-level AV coordination, we adopt a scheduled transportation scheme. The coordinator solves the I-OFOMAC during each decision epoch, optimizing order matching and AV coordination. The I-OFOMAC aims to balance the goals of minimizing AV travel delay and maximizing SAV service revenue. We conduct extensive simulation studies in the realistic Wangjing network to evaluate the proposed I-OFOMAC scheme. Compared to two-step benchmark frameworks that implement order matching and AV coordination sequentially, the I-OFOMAC achieves an average increase of 13.28% in platform revenue (ranging from 2% to 22.53%) and a 4.48% reduction in average PAV travel delay (ranging from −6.55% to 20.17%).

An Innovative Cooperative Driving Strategy for Signal-Free Intersection Navigation with CAV Platoons

We present an innovative cooperative driving strategy known as Dynamic Resequencing and Platooning (DRP) designed to ensure the safe and efficient traversal of Connected and Automated Vehicles (CAVs) through signal-free intersections. By employing a Resequencing and Platooning Algorithm (RPA) grounded in state transition networks and CAV platooning, the optimal crossing sequence for CAVs is ascertained within a finite time. Through the utilization of a decentralized energy-optimal control framework, optimal trajectories are devised for CAVs, thereby facilitating optimal coordination among them. Simulation results underscore the substantial performance benefits of the DRP strategy compared to traffic light, First-In-First-Out (FIFO), and Local Dynamic Resequencing (LDR) strategies, with notable reductions observed in both travel delay and fuel consumption.

Flexible optimal bus‐schedule bridging for metro operation‐interruption

AbstractUnder the sudden interruption of the metro, it is significant to dispatch emergency bridging buses to evacuate stranded passengers to improve linkage management and service reliability. Aiming at the problem of emergency bridging bus scheduling, considering the remaining capacity of conventional buses, passenger tolerance, and site convenience, a flexible combined emergency bridging bus scheduling model is constructed based on the constraints of vehicle capacity, dispatching capacity, and maximum evacuation times of emergency bridging bus, aiming at minimizing the maximum evacuation time and passenger delay. The improved Harris hawk algorithm is used to solve the model, and the evacuation plan of the demand‐responsive and station‐station bridging lines is obtained. The maximum evacuation time is 41 min, and the average delay of stranded passengers is 19 min. The results show that the maximum evacuation time is 10% and 27% less than the fixed combined and single scheduling. The average delay is 16% and 42% less than the fixed combination and traditional single scheduling. The sensitivity analysis of the influencing factors of emergency bridging bus scheduling is conducted. The results show that the flexible combined emergency bridging bus scheduling model constructed in this paper can improve evacuation efficiency and reduce passenger travel delays.

Accounting for traffic disturbance in road infrastructure management: Optimal maintenance and rehabilitation planning for the society

Maintaining a high level of network performance at minimal cost is a central objective for road infrastructure management. Going beyond agency costs, decisions must consider the economic and environmental impacts of maintenance and repairs strategies due to traffic delays on commuters and providers of goods and services. A dynamic programming model is presented to determine optimal management policies for current and proposed paradigms by selecting the best maintenance and rehabilitation activities at each time step over the time horizon given the concomitant and predicted states of the bridges within the network as well as predicted future origin-destination flows. The empirical analysis is demonstrated for a small network of three viaducts in an urban environment in Montréal, Canada. The incremental user costs due to interventions on the structures of the network are obtained through traffic simulations based on trip-survey data to evaluate travel delays for all possible combinations of concomitant partial or complete road closures. The results show that optimal infrastructure management strategies in space and time differ substantially depending on whether only agency costs are used, or if policies also reflect socio-economic goals (including sustainability). The main findings indicate that by including user costs in decision processes, significant reductions in user costs are achieved with minimal impact on operator costs, thus easily satisfying a cost/benefit criterion. The findings provide new evidence for policymakers that the optimal sequencing of interventions in time and space on a network can significantly reduce societal costs, sustainability objectives and environmental impacts.

Measuring the level of urban traffic congestion for sustainable transportation in Addis Ababa, Ethiopia, the cases of selected intersections

Traffic congestion poses a persistent and escalating problem for major cities in both developed and developing countries, exerting a direct impact on the economic growth and development of these urban areas. Quantifying the extent of traffic congestion is the fundamental initial step in comprehending the severity of traffic congestion in order to devise effective methods for alleviation. The city of Addis Ababa is currently experiencing significant traffic congestion at its main intersections. The primary aim of this research is to assess the current level of traffic congestion at specific intersections. The assessment of traffic congestion was conducted using the travel time method. Data on travel time, traffic volume, and travel speed were gathered at three blocks and two intersections using a combination of quantitative and qualitative data collection methods. The travel rate, delay rate, and total travel delay (in vehicle-minutes) were computed. The total vehicle-minute delay for the selected three segments is estimated to be approximately 12,708 vehicle-minutes (or 212 vehicle-hours). The text reveals the significance of measuring the various components of traffic congestion in order to ensure a sustainable traffic system. It also highlights the importance of maintaining a satisfactory level of service for the future sustainability of City.

Mathematical Modeling on Integrated Vehicle Assignment and Rebalancing in Ride-hailing System with Uncertainty Using Fuzzy Linear Programming

The general public frequently uses taxis as local transportation to get from one location to another. Ride-hailing is an innovation in taxi services that lets customers use their smartphones to find drivers, find prices, and submit requests. The two parts of ride-hailing are vehicle assignment and rebalancing. The task of the assignment is to allocate resources as efficiently as possible to fulfil demand. Demand and supply in the area are brought into balance through the rebalancing process. The rebalancing step is frequently carried out independently of the assignment process in ride-hailing pickup systems. However, it can be integrated into a single optimization process to improve system performance. The linear programming approach can integrate assignment and rebalancing. The batch assignment is an assignment algorithm in which each supply and demand is collected within a specific time window. The assignment is done after the vehicles and requests are collected. Fuzzy linear programming is used to deal with environmental uncertainty. Uncertain demand can reduce the reliability of ride-hailing pickup systems in addressing the problem of allocating empty vehicles to areas of high demand. The pickup time may change over time due to traffic conditions. Assignment integration and rebalancing modelling with fuzzy parameters are carried out to obtain a ride-hailing pickup system model that can handle demand uncertainty, pickup travel time, and travel delay times and increase the ride-hailing effectiveness of the pickup system. Numerical simulations were carried out on the assignment and rebalancing integration model without and with uncertainty parameters—numerical simulations based on publicly available taxi travel request data. The model selection for the ride-hailing system can be determined based on the numerical simulation results. This research can potentially advance and expand knowledge, especially in transportation.

Modeling the Reverberation Response of the Broad-line Region in Active Galactic Nuclei

The variable continuum emission of an active galactic nucleus (AGN) produces corresponding responses in the broad emission lines, which are modulated by light travel delays, and contain information on the physical properties, structure, and kinematics of the emitting gas region. The reverberation mapping technique, a time series analysis of the driving light curve and response, can recover some of this information, including the size and velocity field of the broad-line region (BLR). Here we introduce a new forward-modeling tool, the Broad Emission Line MApping Code, which simulates the velocity-resolved reverberation response of the BLR to any given input light curve by setting up a 3D ensemble of gas clouds for various specified geometries, velocity fields, and cloud properties. In this work, we present numerical approximations to the transfer function by simulating the velocity-resolved responses to a single continuum pulse for sets of models representing a spherical BLR with a radiatively driven outflow and a disklike BLR with Keplerian rotation. We explore how the structure, velocity field, and other BLR properties affect the transfer function. We calculate the response-weighted time delay (reverberation “lag”), which is considered to be a proxy for the luminosity-weighted radius of the BLR. We investigate the effects of anisotropic cloud emission and matter-bounded (completely ionized) clouds and find the response-weighted delay is only equivalent to the luminosity-weighted radius when clouds emit isotropically and are radiation-bounded (partially ionized). Otherwise, the luminosity-weighted radius can be overestimated by up to a factor of 2.

Western Montana Snow Squall Climatology and Synoptic Patterns, 2000–2020

The rapid onset of whiteout conditions, from strong winds, blowing snow, and heavy snow, associated with snow squalls often results in hazardous travel conditions. Snow squalls bring pronounced impacts to the transportation sector, leading to accidents, fatalities, and significant travel delays. Given these impacts, historical frequencies, and the synoptic environments associated with snow squalls in western Montana were examined to develop forecast aides, primarily through synoptic pattern recognition, for meteorologists to identify and communicate snow squalls. A 20-yr climatology was constructed to identify events characterized by wind gusts ≥26 kt (30 mph) and visibility ≤0.8 km (0.5 mi). 74 snow squall events were identified from 2000 through 2020 across three ASOS sites: Missoula (KMSO), Kalispell (KGPI), and Butte (KBTM). The median duration of heavy (moderate) snow during snow squalls was 26 (34) min. A composite study utilizing these events was conducted using the NCEP North American Regional Reanalysis. The composite study along with surface observations, revealed three distinct snow squall patterns and types: orographic snow bands, Pacific cold fronts, and Canadian arctic fronts.

Motion Planning for Autonomous Vehicles in Unanticipated Obstacle Scenarios at Intersections Based on Artificial Potential Field

In unanticipated obstacle scenarios at intersections, the safety and mobility of autonomous vehicles (AVs) are negatively impacted due to the conflict between traffic law compliance and obstacle avoidance. To solve this problem, an obstacle avoidance motion planning algorithm based on artificial potential field (APF) is proposed. An APF-switching logic is utilized to design the motion planning framework. Collision risk and travel delay are quantified as the switching triggers. The intersection traffic laws are digitalized and classified to construct compliance-oriented potential fields. A potential violation cost index (PVCI) is designed according to theories of autonomous driving ethics. The compliance-oriented potential fields are reconfigured according to the PVCI, forming violation cost potential fields. A cost function is designed based on compliance-oriented and violation cost potential fields, integrated with model predictive control (MPC) for trajectory optimization and tracking. The effectiveness of the proposed algorithm is verified through simulation experiments comparing diverse traffic law constraint strategies. The results indicate that the algorithm can help AVs avoid obstacles safely in unanticipated obstacle scenarios at intersections.