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Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. This study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide. [ABSTRACT FROM AUTHOR]

Autonomous driving has many positive impacts, such as improving driver and passenger safety, comfort, and traffic efficiency, but all these advantages are based on people's trust and acceptance of this mode of driving. Anthropomorphic driving can enhance the trust and comfort of drivers and passengers and is seen as a feasible measure to increase people's acceptance of autonomous driving. This paper reports the microscopic traffic simulation of three scenarios around a frequently congested intersection, using non-automated vehicles, autonomous driving vehicles, and anthropomorphic driving vehicles to explore their impact on traffic efficiency. The result shows that, compared to non-automated vehicles, both autonomous vehicles and anthropomorphic driving vehicles can improve traffic efficiency during congested periods, increase traffic volume per unit time, reduce the total travel time and time loss, and have a higher average speed. Compared to autonomous vehicles, anthropomorphic driving has a shorter total travel time and a similar time loss. In terms of average speed, anthropomorphic driving performed better than autonomous driving in terms of both congested and non-congested times. In summary, compared to non-automated vehicles, autonomous driving vehicles have a positive effect on improving traffic efficiency, while, compared to autonomous driving, anthropomorphic driving has more advantages in increasing traffic efficiency and reducing traffic congestion. [ABSTRACT FROM AUTHOR]

Given the rapid development of technologies such as new energy vehicles, autonomous driving, and vehicle-to-everything (V2X) communication, a mixed traffic flow comprising connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) is anticipated to emerge. This necessitates the development of a daily dynamic evolution model for mixed traffic flow to address the dynamic traffic management needs of urban environments characterized by mixed traffic. The daily dynamic evolution model can capture the temporal evolution of traffic flow in road networks, with a focus on the daily path choice behavior of travelers and the evolving traffic flow in the network. First, based on the travel characteristics of CAVs and HDVs, the user group in a connected autonomous driving environment is classified into three categories, each adhering to the system optimal (SO) criterion, the user equilibrium (UE) criterion, or the stochastic user equilibrium (SUE) criterion. Next, the pure HDV traffic capacity BPR (Bureau of Public Roads) function is adapted into a heterogeneous traffic flow travel time function to compute the travel time cost for mixed traffic flow. Based on the energy consumption calculation formula for HDVs, the impact of CAVs is fully considered to establish the travel energy consumption cost for both CAVs and HDVs. The total individual travel cost for CAVs and HDVs encompasses both travel time cost and energy consumption cost. Furthermore, a daily dynamic evolution model for mixed traffic flow in a connected autonomous driving environment is developed using the proportional-switch adjustment process (PAP) model. The fundamental properties of the model are validated. Finally, numerical simulations on an N-dimensional (N-D) network confirm the validity and effectiveness of the daily evolution model for mixed traffic flow. A sensitivity analysis of traveler responses in the daily evolution model reveals that, as the sensitivity of CAVs to impedance changes increases, the fluctuations in mixed traffic flow during the early stages of evolution become more pronounced, and the time required to reach a mixed-equilibrium state decreases. Therefore, the PAP-based daily dynamic evolution model for mixed traffic flow effectively captures the evolution process of CAV and HDV mixed traffic flow and supports urban traffic management in a connected autonomous driving environment. [ABSTRACT FROM AUTHOR]

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The application of deep reinforcement learning techniques to adaptive traffic signal control is effective in reducing traffic congestion, due to its advantages over more traditional experience-based or model-driven methods. Although numerous studies have been conducted on traffic signal control, many of them suffer from issues such as unreasonable phase settings and long model-building times in complex environments. By analyzing the spatial-temporal characteristics of intersection traffic volume, this paper puts forward an automatic phase-switching mechanism to improve the problem of phase scheme mismatch with actual traffic flow. To reduce the training time of the model, this paper proposes to add noise to the parameters of the agent to stabilize the environment exploration process, so that the agent’s decision is more dependent on the observed environment state. Using real-world traffic flow data and simulated data for experimental verification, the experimental results show that the signal control model after optimization can effectively reduce the average travel time and increase network throughput. [ABSTRACT FROM AUTHOR]

Herd effect is a widespread phenomenon in real-world situations. This study explores how the herd effect can be used to manage network flow effectively. We examined its impact on travelers' route choices and propose a mixed network flow evolution process that incorporates the herd effect, considering two types of travelers: those who receive route subsidy information and those who do not. Based on this evolution process, we developed a bi-level optimization model to determine the optimal subsidized routes, the subsidy amounts per kilometer, and the proportion of travelers receiving subsidy information. A hybrid algorithm with two iterative procedures was proposed to solve the model, in which the adaptive genetic algorithm (AGA) was employed to solve the upper-level nonlinear mixed-integer programming problem, and the partial linearization method was used to solve the lower-level network flow evolution process. Numerical results indicate that the presence of herd effect can effectively reduce both the total travel time of the network and the overall subsidy costs. The findings of this study have significant implications for the utilization of the herd effect in designing navigation software and developing congestion pricing strategies. [ABSTRACT FROM AUTHOR]

Due to the effects of capacity differences and other factors, congestion may occur at local nodes of a network. This situation reduces the reliability of the network and leads to regional deadlock. To improve system stability and reduce the influence of local road network heterogeneity on traffic efficiency, a macroscopic fundamental diagram (MFD) model of a nonuniform density road network is proposed. Second, based on the MFD model, a two-level strategy for H∞ perimeter control and traffic flow regulation is proposed. The upper layer uses an H∞ controller to adjust the inflow at the boundary of congested region. The lower layer uses a traffic density regulator (TDR) inside the region. Finally,a simulation is conducted to compare with traditional H∞ control, bang-bang-TDR, PI-TDR and MPC-TDR strategy. The results show that the H∞-TDR strategy proposed in this paper can improve operation efficiency and reduce delay in network. [ABSTRACT FROM AUTHOR]

The paper focuses on the calibration of the hybrid multi-scale modelling approach that incorporates multiple levels of traffic flow representation. This paper refers to a model already developed in the literature, the Hybrid Cellular Automata (CA) and Cell Transmission Model (CTM). We use vehicle trajectory data collected from a car-following field experiment on a circular road track to explore the calibration of the CA model concerning various cell lengths through two distinct approaches: simulating all vehicles within the closed loop and simulating each vehicle using data obtained from its respective follower. We also evaluate different methods for the CTM calibration with respect to the CA model. The major findings are: (1) the calibrated parameters obtained using the simulated leader approach display greater regularity across different cell lengths; (2) the Constrained Squared Error [CsqE] method for macroscopic calibrations yielded promising results, showcasing the lowest sum of squared errors between fundamental diagrams. [ABSTRACT FROM AUTHOR]

High-speed railways (HSR) and airlines can complement each other in long-distance journeys served by connecting flights through a hub. To encourage airline-HSR cooperation and promote intermodal transport services, we examine two capacity agreements under the capacity limitations of HSR and/or airlines and their effects on ticket pricing, passenger volume, travel time, frequency, consumer surplus, social welfare, and profitability. In a capacity sell agreement, HSR sells its capacity to airline (HSA), and airline provides the bundling service. In a capacity purchase agreement, HSR purchases airline capacity (HPA) and offers the bundled service. We first analytically compare two capacity agreements with no-cooperation cases, and find that the joint ticket price under the HSA agreement offers a price advantage over individual ticket purchases in non-cooperation scenarios only when HSR is capacity-constrained. Although the HPA agreement also exhibits a similar joint price advantage as the HSA agreement, it requires the stringent condition that both transport modes have limited capacities. Regarding consumer surplus, it tends to increase under the HSA agreement when both transport modes have capacity limitations or both do not have capacity limitations. However, the increasing trend can be relaxed to different capacity limitation conditions under the HPA agreement. We then directly compare the HSA and HPA agreements through simulations. The results indicate that the HSA agreement yields higher consumer surplus and profits than the HPA agreement, while the HPA agreement achieves greater social welfare. • We investigate two types of airline-HSR cooperation agreements initiated by different participants. • We thoroughly examine the impact of airline-HSR cooperation agreements on various factors. • Both types of airline-HSR cooperation agreements can increase total traffic volume. • The optimal airline-HSR cooperation agreement may vary depending on the objectives. [ABSTRACT FROM AUTHOR]

The article offers a solution to traffic congestion caused by temporary disruptions, such as accidents or road construction, through a method called Congestion Management based on Inflow and Outflow Volumes (COMIO). Topics include the limitations of traditional route control methods, the introduction of COMIO to stabilize traffic flow, and how COMIO manages the balance between traffic inflow and outflow to prevent the recurrence of congestion.

Modern urban centers have one of the most critical challenges of congestion. Traditional electronic toll collection systems attempt to mitigate this issue through pre-defined static congestion pricing methods; however, they are inadequate in addressing the dynamic fluctuations in traffic demand. Dynamic congestion pricing has been identified as a promising approach, yet its implementation is hindered by the computational complexity involved in optimizing long-term objectives and the necessity for coordination across the traffic network. To address these challenges, we propose a novel dynamic traffic congestion pricing model utilizing multi-agent reinforcement learning with a transformer architecture. This architecture capitalizes on its encoder-decoder structure to transform the multi-agent reinforcement learning problem into a sequence modeling task. Drawing on insights from research on graph transformers, our model incorporates agent structures and positional encoding to enhance adaptability to traffic flow dynamics and network coordination. We have developed a microsimulation-based environment to implement a discrete toll-rate congestion pricing scheme on actual urban roads. Our extensive experimental results across diverse traffic demand scenarios demonstrate substantial improvements in congestion metrics and reductions in travel time, thereby effectively alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

The acceleration of urbanization has led to increasingly severe traffic congestion, creating an urgent need for effective traffic signal control strategies to improve road efficiency. This paper proposes an adaptive traffic signal control method based on offline reinforcement learning (Offline RL) to address the limitations of traditional fixed-time signal control methods. By monitoring key parameters such as real-time traffic flow and queue length, the proposed method dynamically adjusts signal phases and durations in response to rapidly changing traffic conditions. At the core of this research is the design of a model named SD3-Light, which leverages advanced offline reinforcement learning to predict the optimal signal phase sequences and their durations based on real-time intersection state features. Additionally, this paper constructs a comprehensive offline dataset, which enables the model to be trained without relying on real-time traffic data, thereby reducing costs and improving the model's generalization ability. Experiments conducted on real-world traffic datasets demonstrate the effectiveness of the proposed method in reducing the average travel time. Comparisons with several existing methods highlight the clear advantages of our approach in enhancing traffic management efficiency. [ABSTRACT FROM AUTHOR]

Pine Island Academy, present in Nocatee Community, Jacksonville, Florida, uses various modes of transport for picking up and dropping off children at school. The parents using private vehicles to pick up and drop off their kids tend to take a long time due to delays and congestion in their routes. Parents who use golf carts (GC) for this activity tend to take a short time to pick up and drop off their kids. Due to this, it is necessary to analyze the use of GC as an alternative mode of transport for children pick up and drop off while reducing delay and congestion. This paper uses VISSIM microscopic simulation for analysis whereby the traffic flow in the morning for drop-off and the afternoon for pickup was observed at different GC traffic compositions regarding the relative delay, queue delay, and travel times. From the analysis, it was observed that the delays and travel times for private vehicles decrease as the GC composition increases in the network. These results show that a GC may be suitable for picking up and dropping off children at school. This factor is further supported by the ANOVA test of significance. Due to the results from this paper, recommendations to the transportation agencies responsible for school management include promoting GC usage, establishing designated GC lanes to ensure smooth traffic flow and minimal interaction with other vehicles, implementing a multi-modal approach that includes GCs, and emphasizing safety training and awareness for GC users. [ABSTRACT FROM AUTHOR]

Traffic bottlenecks significantly influence the operation efficiency of large-scale road networks. Developing advanced control strategies for bottleneck optimization is a cost-efficient and critical way to deal with network congestion. However, the state-of-the-art studies on network congestion control focus on the topology level, which may fail to relieve congestion by addressing the root cause of bottleneck. This study proposed a hierarchical control framework for alleviating network traffic bottleneck congestion using vehicle trajectory data. First, the bottleneck-related sub-network (BRS) was identified by tracing vehicle trajectories upstream and downstream of the bottleneck based on the traffic flow propagation. Then, a hierarchical control framework was proposed for BRS optimization. Specifically, in the outer layer, i.e., the gating control layer, the multigated intersections in BRS were controlled via a multimemory deep Q-network approach to optimize the network traffic distribution. In the inner layer, i.e., the coordinated control layer, local intersection controllers were coordinated by adjusting the dynamic input and output streams of the bottleneck under the guidance of the outer layer controller, which helps balance the traffic pressure within BRS and avoids congestion transferring in the network. Both simulation and field experiments were conducted to verify the performance of the proposed hierarchical framework. Results reveal that the framework can effectively relieve network traffic congestion with decreased queue length and travel time. [ABSTRACT FROM AUTHOR]

Random traffic flows and the influence of traffic signals at intersections often cause significant variations in travel times. This has implications for signal control planning and design. This study aims to compare the variability between signalized and unsignalized corridors in Lahore and identify the challenges faced by road users. The study was conducted through a literature survey that collected data on trip purpose, frequency, travel time, and transportation mode choice from various sources. Results show that signalized corridors have lower accident rates and shorter travel times compared to unsignalized corridors. Unsignaled corridors, on the other hand, offer better access and lower fuel costs. Although signalized corridors are more effective in managing traffic flow and improving safety, further research is needed to explore alternative solutions such as roundabouts that can improve mobility and sustainability. The results of this study can serve as a reference for transportation planners and policy makers in designing a more efficient traffic system. This study also suggests the need for further evaluation of traffic control in various urban contexts to optimize safety and efficiency. [ABSTRACT FROM AUTHOR]

In intelligent transportation systems (ITS), achieving accurate travel time estimation (TTE) is paramount, much like route planning. Precisely predicting travel time across different urban areas is vital, and an essential requirement for these privileges is having fine‐grained knowledge of the city. In contrast to prior studies that are restricted to coarse‐grained data, we broaden the scope of traffic flow forecasting to fine granularity, which provokes explicit challenges: (1) the prevalence of inter‐grid transitions within fine‐grained data introduces complexity in capturing spatial dependencies among grid cells on a global scale. (2) stemming from dynamic temporal dependencies. To address these challenges, we propose the multi‐scaling hybrid model (MSHM) as a novel approach. Initially, a multi‐directional convolutional layer is first used to acquire high‐level depictions for each cell to retrieve the semantic attributes of the road network from local and global aspects. Next, we incorporate the characteristics of the road network and coarse‐grained flow features to regularize the local and global spatial distribution modeling of road‐relative traffic flow using an enhanced deep super‐resolution (EDSR) technique. Benefiting from the EDSR method, our approach can generate high‐quality fine‐grained traffic flow maps. Furthermore, to continuously provide accurate TTE over time by leveraging well‐designed multi‐scale feature modeling, we incorporate a multi‐scale feature expression of each road segment, capturing intricate details and important features at different scales to optimize the TTE. We conducted comprehensive trials on two real‐world datasets, BJTaxi and NYCTaxi, aiming to achieve superior results compared to baseline methods. [ABSTRACT FROM AUTHOR]

Shared autonomous electric vehicles (SAEVs) have recently attracted significant public interest. The dynamic ride-sharing using SAEVs appears to have the advantages of reducing travel costs and relieving urban traffic congestion. It is meant to improve the practical application value of the dynamic ride-sharing mode of SAEVs. In this paper, to reduce the solution time complexity, a pre-matching algorithm considering the driverless and charging characteristics of SAEVs is developed, and then a two-stage, graph-based many-to-one ride-matching (GMOM) algorithm is proposed for the dynamic ride-sharing problem in the Autonomous Mobility-on-Demand system (AMOD). The dataset from DiDi during the peak travel time and the real-time traffic flow from the AutoNavi map were used to verify the effects of the method. The results demonstrate that the GMOM approach can effectively reduce computational complexity and improve user satisfaction. The dynamic ride-sharing mode based on the GMOM algorithm has a 5.67% higher service rate and 45.56% more vehicle calls than the non-ride-sharing mode under the same conditions. It is found that the cost-effectiveness of using ride-sharing services is relatively high for 10–20 km trips during peak travel time and the dynamic ride-sharing may extend total travel time but will reduce passengers' waiting time. [ABSTRACT FROM AUTHOR]

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as 'ground truth', the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes. [ABSTRACT FROM AUTHOR]

This paper explores the application of Model Predictive Control (MPC) to enhance safety and efficiency in autonomous vehicle (AV) navigation through optimized path planning. The evolution of AV technology has progressed rapidly, moving from basic driver-assistance systems (Level 1) to fully autonomous capabilities (Level 5). Central to this advancement are two key functionalities: Lane-Change Maneuvers (LCM) and Adaptive Cruise Control (ACC). In this study, a detailed simulation environment is created to replicate the road network between Nantun and Wuri on National Freeway No. 1 in Taiwan. The MPC controller is deployed to optimize vehicle trajectories, ensuring safe and efficient navigation. Simulated onboard sensors, including vehicle cameras and millimeter-wave radar, are used to detect and respond to dynamic changes in the surrounding environment, enabling real-time decision-making for LCM and ACC. The simulation results highlight the superiority of the MPC-based approach in maintaining safe distances, executing controlled lane changes, and optimizing fuel efficiency. Specifically, the MPC controller effectively manages collision avoidance, reduces travel time, and contributes to smoother traffic flow compared to traditional path planning methods. These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios. Future research will focus on validating these results through real-world testing, addressing computational challenges for real-time implementation, and exploring the adaptability of MPC under various environmental conditions. This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation, paving the way for broader adoption of MPC in AV systems. [ABSTRACT FROM AUTHOR]

The advent of automated vehicles (AVs) and connected automated vehicles (CAVs) creates significant uncertainties in infrastructure planning due to many unknowns, such as performance variability and user adaptation. As technologies are still emerging with low market penetration, limited observational data hinder validation and escalate prediction uncertainty. This study addresses these gaps by employing diverse vehicle models and wide performance ranges in Aimsun microsimulations. It involved three AV/CAV car‐following models with the default Gipps human‐driven vehicle (HDV) model. We evaluated the performance of a mixed fleet in three well‐calibrated real‐world corridor models, including two highways and one freeway. Vehicle parameters in Aimsun are commonly drawn from a corresponding truncated normal distribution with fixed mean, min, and max values. However, to account for future uncertainty and heterogeneity, our AV/CAV models were given truncated normal distributions with variable means for important parameters to incorporate broader performance ranges. The variable means are drawn from intervals with uniform probability, and some of the interval extended below HDV values to account for scenarios where riders opt for smoother rides at the cost of traffic flow. Recognizing that precise future prediction is unattainable, we aimed to establish traffic performance boundaries that define best‐ and worst‐case scenarios in a mixed‐fleet environment. Enumerating all possible combinations is impractical, so a refined optimization algorithm was employed to expedite solution discovery. Our findings suggest that AVs/CAVs, even with conservative performance parameters, can improve traffic operations by reducing peak delays and enhancing travel time reliability. Freeways benefited more than arterial roads, especially with full CAV penetration, although the authors speculate this could create bottlenecks at off‐ramps. The added capacity may induce traffic demand that is difficult to estimate. Instead, we conducted a demand sensitivity analysis to gauge additional traffic accommodation without worsening delays. Compared to point predictions, establishing the range of possibilities can help us future‐proof infrastructure by considering uncertainties in the planning process. Our framework can be adopted to test alternative models or scenarios as more data becomes available. [ABSTRACT FROM AUTHOR]

To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems. [ABSTRACT FROM AUTHOR]

The Cooperative Intelligent Transport System (C-ITS) aims to address traffic efficiency, road safety, and environmental sustain-ability by facilitating communication among autonomous entities with complex behaviors. Traffic congestion is a significant issue in modern urban areas, influenced by factors like accidents, road works, weather and peak hours, leading to wasted time, fuel, and increased pollution. To address these challenges, multi-agent systems have proven effective in managing dynamic traffic in C-ITS environments. In this paper, we propose a novel Reinforcement Learning Agent (RLA) method for decision-making in C-ITS systems to optimize traffic flow and control. Our method utilizes the Q-learning algorithm, enabling effective problem-solving. We conducted experiments to demonstrate the versatility and effectiveness of our approach in complex C-ITS environments. Our method surpasses traditional metrics like mean travel time and mean speed, validated through comparisons with Actor-Critic methods and against the Original Traffic Trace (OTT). This research contributes to advancing decision-making capabilities within C-ITS systems by leveraging RLA for traffic optimization and efficiency. [ABSTRACT FROM AUTHOR]

Mainline coordinated control is usually based on fixed speed and statistical traffic flow by period. However, in actual operation, the vehicles parked in front of the intersection and the arriving vehicles often fluctuate, and the through-traffic green time is wasted due to phase transition, which leads to mismatches between the signal plans and actual traffic flow requirements, affecting the traffic efficiency of the intersection. To address the above issues, using vehicle–road collaborative control (VRCC), by calculating the phase difference lead time and phase difference of adjacent intersections, the green extension time for the green wave through-traffic phase, and the guiding vehicle speed, the goal of reducing the detention volume of through traffic, reducing the waste of through-traffic green time caused by phase transitions and improving the throughput of through traffic can be achieved. The speed of the green wave traffic flow is increased by guiding vehicles to form saturated platoons during green periods. Finally, PTV VISSIM 4.3 was used for simulation verification, and the results showed that compared to not implementing the control strategy, the average delay on the arterial road was reduced by 85.1%, the average number of stops was reduced by 84.3%, the average travel time was reduced by 34%, and the average queue length was reduced by 62.6%. This significantly improved the efficiency of traffic on the arterial road and effectively reduced congestion. [ABSTRACT FROM AUTHOR]

Introduction: The rise of autonomous vehicles has brought about a transformative shift in transportation, witnessing the coexistence of human-driven and autonomous vehicles on highways in the United States, Europe, and China. This coexistence poses challenges to traffic operations, particularly in intricate scenarios like highway ramps. The interaction between autonomous truck platoons, displaying heightened maneuverability, and human-driven vehicles has emerged as a critical concern. Consequently, this research aims to propose and investigate three avoidance modes (overall, gap and cross) employed by truck platoons, evaluating their comprehensive impact on human-driven vehicles. Methods: Multiple scenarios are simulated utilizing the Simulation of Urban Mobility (SUMO) software, collecting data on three distinctive avoidance modes concerning Travel Time (TT) and Time to Collision (TTC). Employing principles of game theory, a comprehensive assessment is undertaken to evaluate the traffic efficiency and safety of each mode. Comparative analyses against a noavoidance baseline are conducted, offering a holistic evaluation of each mode's applicability across diverse scenarios. Results: The findings highlight the commendable performance of gap mode and overall mode in enhancing traffic efficiency, while cross mode excels in fortifying traffic safety. Overall, the gap mode emerges as the optimal choice among the three. Discussion: This study introduces a game-theoretic approach to managing humanmachine mixed traffic flow, establishing a foundational framework for theoretical research in decision-making for emerging mixed traffic environments. It considers safety and efficiency perspectives across different types of traffic entities. The insights gained contribute to the evolving discourse on the integration of autonomous vehicles into existing traffic systems, addressing the intricate challenges posed by the coexistence of various vehicle types on highways. [ABSTRACT FROM AUTHOR]

This paper develops a dynamic procedural model to understand the day-to-day evolution of road traffic and risky mode choice behaviour. We embed a modified Bureau of Public Roads function and a queueing model into a dynamic traffic flow model to estimate information on travel time and in-vehicle crowding. Then, the estimated information is fed into a risky mode choice model, with the joint consideration of intra- and inter-individual risk attitude heterogeneity. In order to reflect what may be happening in the real market, we establish a close loop with individuals' mode choice behaviours shaped by their travel experiences and historical information, the induced impact on traffic flow, and more importantly, the feedback and interplay between them. Our model delivers realistic and robust results, in which risk attitudes play the most prominent role. [ABSTRACT FROM AUTHOR]

Many researchers argue that autonomous vehicles (AV) will create a safer and more efficient transport infrastructure. However, some studies have drawn attention to potential problems in relation to how AVs and human-driven vehicles will co-exist. We investigated the effect of speed compliance behavior of AVs on an urban two-lane road section with alternating speed limits of 50 and 30 km/h. A Vissim microsimulation model was used where we gradually increased the traffic volume and the market penetration rate (MPR) of AVs. We found that under low traffic flow conditions the increase in MPR will lead to a decrease in the average speed and worsening of the average travel time. Under medium and, specifically, under saturated conditions, a high share of AVs can bring stability to the system. They can also significantly improve the speed compliance rate. [ABSTRACT FROM AUTHOR]

In areas with significant changes in traffic demand and high vehicle dispersion at adjacent intersections, such as the surrounding roads of large shopping malls and schools, traffic problems are prone to occur. This is due to the unequal signal cycle lengths used at upstream and downstream intersections, which lead to periodic phase offsets as the cycles progress. To address this, we propose a multi-strategy integrated vehicle–road coordinated control method to tackle traffic flow operational issues caused by the offset characteristics of unequal-cycle adjacent intersections. A multi-strategy combined algorithm and control logic is established, which includes downstream intersection coordinated phase green extension, dynamic offset adjustment, and transitional queue speed guidance. The proposed method can substantially minimize the offset from falling into an incompatible threshold, effectively reducing queuing and early arrival of vehicles in the straight-through direction. It enables arriving vehicles to pass through the intersection without or with minimal stopping. Finally, the effectiveness of the method is validated using simulation experiments. A vehicle–road coordinated simulation verification platform was established, and comparative experiments were designed. The results indicate that the multi-strategy combined vehicle–road coordinated control method proposed in this paper, while ensuring the original through capacity for straight movements, can effectively reduce queue lengths, the number of stops, average vehicle delay, and travel time for single-direction straight lanes. This improvement enhances the efficiency of coordinated movements in the unequal–cycle adjacent intersections. [ABSTRACT FROM AUTHOR]

In India, buses play a pivotal role in public transportation, yet commuters often face delays in their daily travels. To address this, Queue Jump Lane (QJL) interventions at intersections have been proposed. Existing studies focus on reducing bus delays, often at the expense of increased travel times for non-priority vehicles, and predominantly consider homogeneous traffic conditions. This study aims to identify an optimal QJL scenario, evaluating its performance across varying Volume-to-Capacity (v/c) ratios to minimize bus travel time while causing minimal disruptions to other modes of transportation. Conducting research in Guwahati, a city with mixed traffic conditions, data on road geometry, traffic signals, and vehicular characteristics were collected from two signalized intersections. Using VISSIM software, a Traffic Microsimulation Model (TMM) was developed to simulate Priority Scenarios (PS) representing various QJL configurations, including bus advance areas and pre-signals. The model underwent calibration through sensitivity analysis, employing Oneway Analysis of Variance (ANOVA), and validation based on the Root Mean Square Normalized Error (RMSNE) using Traffic volume as the Measure of Effectiveness (MOE). Simulation results highlight that PS10, incorporating a curb-side QJL with pre-signal, a fully utilized 75m bus advance area, and a nearside bus stop, can be able to reduce bus travel time by 39% with minimal impact on non-priority vehicles. However, PSIO's efficiency diminishes when v/c ratios exceed 1. This study provides valuable insights for transport planners and practitioners, assisting in the selection and implementation of optimal QJL scenarios tailored to specific intersections and prevailing v/c ratios. This research contributes to enhancing public transportation efficiency while considering the complexities of mixed traffic conditions. [ABSTRACT FROM AUTHOR]

Travel time reliability is known to be a critical issue in the contexts of both travellers' choices and decisions and freight transportation. The temporal variability of travel time is known as reliability and is affected by numerous factors. Traffic volume, incidents and inclement weather are among the most profound factors, and their effects have been the subject of many studies. The work reported in this article is unique due to the simultaneous implementation of a genetic algorithm (GA) with multiple machine learning (ML) methods. A GA can eliminate overfitting, which is a common problem in ML models. The numerical results showed that the performance of the K-nearest neighbours method was significantly enhanced when a GA was imposed on it. In terms of the stability ratio, a 12% decrease was observed; the mean squared errors for the training set and the testing set decreased, but the reductions were not significant. To further illustrate the advantages of GA implementation, the numbers of predictions with a mean absolute percentage error greater than 0.05 were compared and a notable reduction was found. Sensitivity analysis was carried out to determine how the planning time index responds to fluctuations of independent variables. [ABSTRACT FROM AUTHOR]

This study focuses on investigating the significant impacts of speed breakers on various parameters, including travel time delays, vehicle speeds, fuel consumption, pavement maintenance costs, and vehicular exhaust emissions. Field data was collected and analyzed to assess the effects of different types of traffic calming measures on these parameters. The findings provide valuable insights into the implications of speed breakers on road safety, environmental pollution, and overall road infrastructure management. The results reveal that the implementation of speed humps, speed bumps, and triple bumps effectively slows down vehicles, as evidenced by considerable reductions in the 85th percentile speeds. The reduction percentages were 41.65% for speed humps, 73.52% for speed bumps, and 86.27% for triple bumps. This indicates the effectiveness of these traffic calming measures in improving road safety by reducing vehicle speeds. However, the presence of speed breakers also leads to increased travel time delays. On average, traversing stretches with speed humps, speed bumps, and triple bumps resulted in delays of 9.31, 16.42, and 29.51 seconds, respectively. While the individual delay times may appear relatively short, the cumulative effect of multiple speed obstacles along a road needs to be considered. Another significant impact observed is the increased fuel consumption associated with speed breakers. The study found that for every 100 km of travel, motorcycles and passenger cars consumed approximately 12.07 km and 27.37 km of additional fuel, respectively, when the density of speed breakers was 1.33/km. This translates to a fuel consumption increase of 13.73% for motorcycles and 37.74% for passenger cars. Furthermore, the presence of speed humps was found to contribute to pavement deterioration, as indicated by decreased Pavement Condition Index (PCI) values. The study also revealed that sections with speed humps incurred significantly higher maintenance costs compared to sections without speed humps. The increase in maintenance cost ranged from 100 to 264% across different road sections, with higher traffic volumes leading to greater cost escalation. Additionally, the study confirms that lower vehicle speeds, particularly between 0-15 km/hr, are associated with higher emissions of pollutants, including carbon monoxide (CO) and other pollutants. This highlights the environmental implications of speed breakers and their contribution to urban air pollution. [ABSTRACT FROM AUTHOR]

This study delves into the effects of autonomous vehicle behaviors on travel times and vehicle arrivals along the Kirkuk-Sulaymaniyah Highway, employing simulations spanning 3600, 5400, and 7200 seconds. Across varied traffic volumes ranging from 350 to 950 vehicles and autonomous vehicle behaviors categorized as cautious, normal, aggressive, aggressive platoons, and a mix alongside human-driven vehicles, the research unveils significant findings. Results highlight substantial reductions in average travel times and heightened vehicle arrivals among autonomous vehicles, particularly those exhibiting aggressive behaviors, compared to their human-driven counterparts. Across all simulation scenarios, aggressive autonomous vehicles consistently demonstrate superior performance, showcasing potential efficiency gains through aggressive driving algorithms. Furthermore, with increasing traffic volume, the advantages of aggressive autonomous behaviors become more pronounced, suggesting their adaptability to congested conditions. However, safety implications and traffic flow dynamics warrant caution, especially in scenarios with high volumes and aggressive behaviors. These insights underscore the importance of further research and policy considerations to leverage the full potential of autonomous vehicles while ensuring safety and efficiency on highways. [ABSTRACT FROM AUTHOR]

This study delves into quantifying the adverse effects of road damage on users, particularly focusing on the increased travel time and consequent financial burdens stemming from delayed repairs. Utilizing a comparative method, the research underscores notable reductions in speed and prolonged travel times due to damaged roads, leading to substantial economic losses for road users. To streamline the estimation of road user costs (RUC), the study proposes a simulation model that incorporates varying traffic volumes and repair delays. This model demonstrates a high level of accuracy in estimating RUC, revealing heightened sensitivity to fluctuations in traffic volume and repair delays compared to agency costs. Consequently, the research underscores the imperative of implementing effective repair strategies to alleviate these impacts efficiently, thereby emphasizing the significance of timely infrastructure maintenance in mitigating financial burdens on road users. [ABSTRACT FROM AUTHOR]

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Traffic flow on highways is a dynamic process in which characteristics of road segments vary in time and space. Traffic congestion is the adverse effect caused dby an increase in travel time which is unprecedented by time and space. Temporal and spatial information of traffic flow is an integral component in the assessment of highway traffic flow. The spatial–temporal traffic flow dependency on highways can be well assessed when temporal traffic information in preceding time instances is sequenced. Thus, the SCAE-LSTM network is proposed considering time and space. This study investigates the estimation of traffic flow on highways based on spatial and temporal traffic sequences. Sequencing highway traffic information has motivated the authors to propose the method. The performance of the method is experimented on State Highways SH 49 and SH 49-A of Chennai Metropolitan City, Tamil Nadu, India. The computational complexity of the method is analyzed empirically. The significant outcome of the proposed method is reported in the experimental study. The traffic flow estimated using the proposed method has shown reduced complexity compared to other baseline methods. Finally, research directions to work in future are presented towards the end. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we consider a novel reliability-based network equilibrium problem for mixed traffic flows of human-driven vehicles (HVs) and connected autonomous vehicles (CAVs) with endogenous CAV market penetration and stochastic link capacity degradations. Travelers' perception errors on travel time and their risk-aversive behaviors on mode choice and path choice are incorporated in the model with a hierarchical choice structure. Due to the differences between HVs and CAVs, the perception errors and the safety margin reserved by risk-averse travelers are assumed to be related to the vehicle type. The path travel time distribution is derived by using the moment-matching method based on the assumption that link capacity follows lognormal distribution and link travel times are correlated. Then, the underlying problem is formulated as an equivalent variational inequality problem. A path-based algorithm embedded with the Monte Carlo simulation-based method is proposed to solve the model. Numerical experiments are conducted to illustrate the features of the model and the computational performance of the solution algorithm. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles can reduce the traffic congestion level of the entire network through platoon-driving technologies compared to human-driven vehicles. One promising approach to enhancing platoon-driving technology's efficiency is deploying dedicated lanes or roads for connected and automated vehicles. Since asymmetric interactions between different vehicle types increase road congestion, it is necessary to distinguish routes for efficient traffic management. However, the traditional traffic assignment problem, which uses only user equilibrium as a constraint with no difference in travel time between users, could not be proposed as a globally optimal solution because it generates an infinite number of locally optimal solutions. Recent studies have attempted to overcome the limitations by considering the sum of system-wide travel times as an additional constraint. Their research sought to help propose optimal deployment strategies through the lowest total travel time solution (best-case) or design robust transport planning strategies through the highest total travel time solution (worst-case). However, past studies have not focused on the possibility of the best/worst case appearing in reality. This study focused on the relationship between the two solutions pointed out in past studies and traffic patterns likely to appear in reality. This study interprets the Karush–Kun–Tucker condition of the static traffic assignment problem, considering the asymmetric interaction, and proposes a solution algorithm using discrete dynamics. The proposed algorithm extends the most widely used method in transportation planning research, which can overcome the limitations of asymmetric interaction problems through simple variations. The proposed algorithm can reliably derive two solutions, and entropy theory shows that both solutions are unlikely to appear in reality without additional policies such as dedicated lanes or roads. [ABSTRACT FROM AUTHOR]

The weaving section on the freeway will be an active bottleneck due to vehicles' mandatory or discretionary lane-changing (LC) conflicts. In the early stages of the development of intelligent connected transportation, destination information collection of weaving vehicles is possible, which provides an opportunity to improve performance in weaving traffic. In this paper, a simulation system with coordinated strategies embedded to satisfy the LC needs is presented under the weaving traffic flow. First, an algorithm is proposed to evaluate the balance between the demand for LC vehicles and the available gaps provided in weaving traffic, which can be adjusted by variable speed limit (VSL) and ramp metering strategies. Second, to validate the proposed algorithm, a typical weave section with a merge and a diverge connected through a single auxiliary lane is researched, and a simulation platform based on cellular automata has been developed. The optimal control strategies are obtained by model predictive control (MPC) and taking the minimum travel time as an objective. Third, the advantages of coordinated control strategies are studied and discussed in traffic efficiency improvement and the success of LC proportions under different combinations of weaving ratios and large vehicle traffic mix. [ABSTRACT FROM AUTHOR]

The present study aims at investigating the main and interaction effects of contributing factors from multiple risk sources (i.e. traffic flow characteristics, roadway design and weather conditions) on the real-time crash risk for urban roads. A mixed logit model is used to characterise heterogeneity induced by unobservable factors, and result shows that the mixed logit model achieves better goodness-of-fit than the binary logit model. It is found that the multiple risk sources such as roadway design (i.e. length of road segment, road hierarchy), traffic flow characteristics (i.e. average speed, average travel time, congestion level), and weather conditions (i.e. wind speed) have significant effects on the real-time crash risk. Moreover, interactions between the multiple risk sources such as roadway design and traffic flow characteristics (i.e. SD of speed and length of road segment, road hierarchy and average travel time), traffic flow characteristics and weather conditions (i.e. average travel time, temperature and wind speed), and roadway design and weather conditions (i.e. length of road segment and wind speed) are found to be significantly correlated to the real-time crash risk. Such findings can be potentially used for the development of real-time management measures that could mitigate the high-risk situations of urban roads. [ABSTRACT FROM AUTHOR]

Enhancing the efficiency of intercity transportation networks is crucial for sustainable regional transport development, significantly impacting travel behaviors and energy consumption. The transportation infrastructure within the city cluster is rapidly developing to accommodate the increasing traffic demand, necessitating substantial investments. It is imperative to investigate the effectiveness of intercity traffic within urban clusters, to evaluate the influence of transportation infrastructure enhancements on regional traffic efficiency. Circuity is a conventional metric used to assess the efficiency of transportation networks, primarily emphasizing distance, while overlooking factors such as travel time and traffic flow. In this study, the concept of circuity has been redefined in terms of travel time and has been referred to as the transportation network travel speed. Subsequently, the amalgamation of travel speed within the transportation network and traffic flow culminates in the proposition of Flow-Weighted Time Circuity (FWTC). Real-time intercity navigation data, offering accurate travel time estimations, are utilized to analyze the spatial distribution of intercity transport efficiency in the seven major city clusters of China, via both automobile and train modes of transportation. The results indicate that (1) as the travel distance extends, the speed of transportation within the network typically increases, albeit with increasing fluctuations, especially in the case of intercity train travel; (2) concerning the efficiency of intercity automobile travel, most city clusters demonstrate satisfactory performance, with the exception of the Guanzhong Plain. The Yangtze River Delta and Beijing–Tianjin–Heibei regions stand out for their superior performance. In terms of intercity train efficiency, the Yangtze River Delta, Beijing–Tianjin–Heibei, and Mid-Yangtze River regions exhibit higher levels of efficiency in intercity train transportation, while the Guanzhong Plain city cluster falls behind in this aspect. On the whole, the efficiency of intercity travel using automobiles surpasses that of train travel, indicating a pressing need for improvement in the latter. [ABSTRACT FROM AUTHOR]

The utilization of autonomous vehicles (AVs) has emerged as a pivotal factor in addressing the rising costs and safety concerns associated with modern travel. As technology advances and traffic challenges intensify, enhancing accessibility stands out as a critical goal for transportation experts. Accessibility, constrained by factors like travel time, underscores the increasing need for AVs to mitigate these limitations. This study aimed to model the influence of AVs on the accessibility index within transportation networks and discuss system optimization based on user equilibrium (UE) and system optimum (SO) outcomes. The research conducted numerical analysis employing the Hearn network as a fundamental system to validate a mixed assignment model and ascertain baseline accessibility. Additionally, the Sioux Falls network, a medium-sized network, was employed for analysis. A hybrid heuristic assignment algorithm was introduced, concurrently assigning different percentages of AV presence alongside the remaining non-AV percentage in three distinct scenarios. These scenarios ranged from 0 to 100% AV presence: the first scenario maintained constant network capacity, the second scenario adjusted network capacity based on AV presence, and the third scenario incorporated capacity adjustments in the assignment stage. In all three scenarios, network accessibility was evaluated using gravity and accessibility index methods derived from the hybrid assignment model output. The findings demonstrated that as the percentage of AVs increased, accessibility improved in both Hearn and Sioux Falls networks across all scenarios. The second and third scenarios exhibited higher accessibility increases compared to the first, attributable to augmented capacity resulting from increased AV presence. In the Sioux Falls network, the first scenario showed enhanced SO and UE due to increased AV presence and enhanced system operator management. Conversely, the second and third scenarios, with increased AVs and subsequent capacity increments, displayed reduced UE and SO results. Despite the decline in UE and SO, traffic flow assignment and overall network accessibility improved. These findings highlight the positive correlation between AV presence, network capacity, and enhanced accessibility. The study underscores the potential benefits of AV integration in optimizing transportation networks and improving overall accessibility, albeit with nuances in capacity adjustments impacting traffic flow dynamics. Further research avenues could explore complex traffic flow scenarios and delve into more specific optimization strategies. [ABSTRACT FROM AUTHOR]

To address urban traffic congestion, researchers have made various efforts to mitigate issues such as prolonged travel time, fuel wastage, and pollutant emissions. These efforts primarily involve microscopic route selection from the vehicle perspective, multi-vehicle route optimization based on traffic flow information and historical data, and coordinated route optimization that models vehicle interaction as a game behavior. However, existing route selection algorithms suffer from limitations such as a lack of heuristic, low dynamicity, lengthy learning cycles, and vulnerability to multi-vehicle route conflicts. To further alleviate traffic congestion, this paper presents a Period-Stage-Round Route Selection Model (PSRRSM), which utilizes a population game between vehicles at each intersection to solve the Nash equilibrium. Additionally, a Period Learning Algorithm for Route Selection (PLA-RS) is proposed, which is based on a multi-agent deep deterministic policy gradient. The algorithm allows the agents to learn from the population game and eventually transition into autonomous learning, adapting to different decision-making roles in different periods. The PSRRSM is experimentally validated using the traffic simulation platform SUMO (Simulation of Urban Mobility) in both artificial and real road networks. The experimental results demonstrate that PSRRSM outperforms several comparative algorithms in terms of network throughput and average travel cost. This is achieved through the coordination of multi vehicle route optimization, facilitated by inter-vehicle population games and communication among road agents during training, enabling the vehicle strategies to reach a Nash equilibrium. [ABSTRACT FROM AUTHOR]

With the development of vehicle-road network technologies, the future traffic flow will appear in the form of hybrid network traffic flow for a long time. Due to the change in traffic characteristics, the current hard shoulder running strategy based on traditional traffic characteristics cannot effectively serve the hybrid network traffic flow scenario, and will even lead to the further deterioration of traffic congestion. In order to propose a hard shoulder running strategy suitable for a hybrid network environment, a traffic breakdown prediction method based on a hidden Markov model was established. Secondly, the characteristics of traffic breakdown in a hybrid network environment were analyzed. Finally, based on the traffic breakdown characteristics in a hybrid network environment, a dynamic hard shoulder running method based on the hidden Markov model was proposed. The effectiveness of HMMD-HSR was verified by simulation and comparison with HMM-HSR, LMD-HSR, and N-HSR. The simulation results show that the HMMD-HSR proposed in this paper can improve operation efficiency and reduce travel time in a congested expressway. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Researchers widely use the two-fluid model (TFM) to evaluate the performance of urban networks. However, the TFM is deterministic and does not capture the stochastic relation between speed and density. The present study develops a modified two-fluid model (MTFM). The variance function or the distribution of speed or travel time for a given density is incorporated using a percentile-based indicator, travel time uncertainty (TTU). The percentile-based indicators for the speed distribution are more robust than the variance or other moment-based indicators. The effect of TTU is incorporated using two parameters, $$\alpha ,$$ α , and $$ \beta $$ β. The applicability of the proposed MTFM is demonstrated using empirical data collected at the corridor and network levels. The TFM and MTFM were calibrated by formulating a nonlinear optimization problem. Based on the investigation using the corridor and network-level data, it was concluded that the MTFM showed better performance than the existing model.. [ABSTRACT FROM AUTHOR]

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households' and individuals' long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model's flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond. [ABSTRACT FROM AUTHOR]

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane-changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane-changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane-changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two-lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane-changing location matching scheme can save 5.9% total travel time when compared to the near-end location lane-changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane-changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane-changing vehicles increase, our method could perform better. [ABSTRACT FROM AUTHOR]

City-scale traffic data, such as traffic flow, speed, and density on every road segment, are the foundation of modern urban research. However, accessing such data on a city scale is challenging due to the limited number of sensors and privacy concerns. Consequently, most of the existing traffic datasets are typically limited to small, specific urban areas with incomplete data types, hindering the research in urban studies, such as transportation, environment, and energy fields. It still lacks a city-scale traffic dataset with comprehensive data types and satisfactory quality that can be publicly available across cities. To address this issue, we propose a unified approach for producing city-scale traffic data using the classic traffic assignment model in transportation studies. Specifically, the inputs of our approach are sourced from open public databases, including road networks, traffic demand, and travel time. Then the approach outputs comprehensive and validated citywide traffic data on the entire road network. In this study, we apply the proposed approach to 20 cities in the United States, achieving an average correlation coefficient of 0.79 in average travel time and an average relative error of 5.16% and 10.47% in average travel speed when compared with the real-world data. [ABSTRACT FROM AUTHOR]

The present study contributes to narrowing down the research gap in modeling individual door-to-door trips in a superblock scenario and in evaluating the respective impacts in terms of travel times, modal shifts, traffic performance, and environmental benefits. The methods used are a multiple-criteria approach to identify the superblocks and a large-scale, multi-model, activity-based microscopic simulation. These methods were applied to the city of Bologna, Italy, where 49 feasible superblocks were identified. A previous large-scale microscopic traffic model of Bologna is leveraged to build a baseline scenario. A superblock scenario is then created to model five proposed traffic intervention measures. Several mobility benefit indicators at both citywide and superblock levels are compared. The simulation results indicate a significant increase in walking time for car drivers, while the average waiting time of bus users decreases due to the increased frequency of bus services. This leads to a noticeable car-to-bus shift. In addition, absolute traffic volumes and traffic-related emissions decreased significantly. Surprisingly, traffic volumes on the roads around the superblocks did not increase as expected. In general, this research provides scientists and urban and transport planners with insights into how changes in door-to-door travel times of multi-modal trips can impact individual travel behavior and traffic performance at a citywide level. However, the study still has limitations in modeling the long-term effects regarding changing activity locations within the superblocks. [ABSTRACT FROM AUTHOR]