Travel Delay Research Articles

Performance Evaluation of Reinforcement Learning-Based Adaptive Signal Control Deployment in Urban Network

The adaptive traffic control system (ATCS) has shown great potential to address traffic congestion, fuel consumption, and air pollution issues because of its strong adaption to traffic fluctuation. With the popularity of artificial intelligence, Reinforcement Learning (RL) has been applied to ATCS and achieved excellent performance. In RL-based ATCS, the controller collects traffic state information and learns a better signal control policy by minimizing travel delay. For ATCS of an area-wide network, most existing studies assumed that all intersections are installed with ATCS. However, fully ATCS networks are difficult to achieve in the real world due to the high upgrade cost. Therefore, a practical ATCS deployment strategy in a network is needed for agencies with limited budgets. This study proposes a concept of penetration rate, or the percentage of intersections with ATCS in an area, for intelligent intersections. The weighted PageRank algorithm determines a reasonable deployment plan based on road connectivity and link volume. The relationship between penetration rate and traffic performance is investigated in the SUMO simulation. It reveals that the average stopped and control delays gradually decreased with the penetration rate increase. When the penetration rate reaches 75%, the ATSC performance is about 90% of the optimum. However, inequity and inefficiency exist in non-intelligent intersections regarding average queueing time and length at the penetration rate below 40%. Consequently, the suitable range of penetration rate should be between 40% and 75% to balance the costs and benefits of ATCS deployment with budget constraints.

Feasibility Study of a Motorcycle Lane: Evaluation of Travel Time and Delay Impacts at New Baneshwor Intersection

Like many South Asian countries, Nepal can be identified as a “Motorcycle Dependent Country” as the chief mode of transportation on the urban streets of Nepal compromises of Motorcycles for their daily commute. With inefficient public transportation, haphazard lane discipline, and easy accessibility and mobility of PTW, there is a rising dependence on PTW. The risk of life that comes with this dependence is also increasing in the urban streets of the country. Therefore, this research proposes Motorcycle Lanes as a measure to reduce congestion and create a risk-free riding environment. Three scenarios– a) Considering Dedicated motorcycle lanes b) Excluding right turning traffic into dedicated motorcycle lanes c) Dedicated motorcycle lanes only for two intersection legs Maitighar-Tinkune and Tinkune-Maitighar have been proposed, analyzed and compared with base data to measure the efficiency of motorcycle lanes at the New-Baneshwor intersection. The feasibility of those scenarios was checked based on traffic volume, Q-length, and vehicle delay with base data obtained from simulation in VISSIM. It was observed that difficult turning movements, unpredictable riding behavior and signal phases rendered the lanes unfeasible. A need for a holistic approach to address current and future traffic conditions as PTW are poised to experience a high upswing in foreseeable future is an essence.

Network-wide safety impacts of dedicated lanes for connected and autonomous vehicles

Cooperative, Connected and Automated Mobility (CCAM) enabled by Connected and Autonomous Vehicles (CAVs) has potential to change future transport systems. The findings from previous studies suggest that these technologies will improve traffic flow, reduce travel time and delays. Furthermore, these CAVs will be safer compared to existing vehicles. As these vehicles may have the ability to travel at a higher speed and with shorter headways, it has been argued that infrastructure-based measures are required to optimise traffic flow and road user comfort. One of these measures is the use of a dedicated lane for CAVs on urban highways and arterials and constitutes the focus of this research. As the potential impact on safety is unclear, the present study aims to evaluate the safety impacts of dedicated lanes for CAVs. A calibrated and validated microsimulation model developed in AIMSUN was used to simulate and produce safety results. These results were analysed with the help of the Surrogate Safety Assessment Model (SSAM). The model includes human-driven vehicles (HDVs), 1st generation and 2nd generation autonomous vehicles (AVs) with different sets of parameters leading to different movement behaviour. The model uses a variety of cases in which a dedicated lane is provided at different type of lanes (inner and outer) of highways to understand the safety effects. The model also tries to understand the minimum required market penetration rate (MPR) of CAVs for a better movement of traffic on dedicated lanes. It was observed in the models that although at low penetration rates of CAVs (around 20%) dedicated lanes might not be advantageous, a reduction of 53% to 58% in traffic conflicts is achieved with the introduction of dedicated lanes in high CAV MPRs. In addition, traffic crashes estimated from traffic conflicts are reduced up to 48% with the CAVs. The simulation results revealed that with dedicated lane, the combination of 40-40-20 (i.e., 40% human-driven – 40% 1st generation AVs– 20% 2nd generation AVs) could be the optimum MPR for CAVs to achieve the best safety benefits. The findings in this study provide useful insight into the safety impacts of dedicated lanes for CAVs and could be used to develop a policy support tool for local authorities and practitioners.

Reservation-based traffic signal control for mixed traffic flow at intersections

Mixed traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) will be a new state in the future traffic pattern. However, the inherent “stop-and-go” mode of existing traffic signal control seriously weakens the advantage of CAV cooperative operation in mixed traffic. To handle this problem, this study proposes a novel traffic signal control scheme that addresses a mixed traffic scenario at a typical intersection. Specifically, the control scheme can precisely match traffic demand and space-time supply. Then, a protocol of white phases is proposed to decrease the crossing time of CAVs. Finally, the traffic signals and vehicle trajectories are optimized in a unified control framework that minimizes the travel delay of all vehicles. The proposed scheme is verified in SUMO-based simulation considering various penetration rates of the CAVs, different input traffic volumes, and different proportions of left-turning traffic. The experimental results show that the proposed scheme is superior to existing actuated signal control regarding stability when facing different input traffic volumes and proportions of left-turning traffic. Moreover, it is observed that the travel delay decreases with the penetration rates of the CAVs, indicating the potential of the proposed method in the near future mixed traffic scenario.

Sistem Informasi Monitoring Pengiriman Kargo DAMRI Logistik Lintas Jawa - Sumatera Berbasis Web

Cargo delivery is an integral part of logistics operations, where monitoring plays a crucial role. Manual approaches in monitoring increase the risk of human error and prolong the process duration. Major issues frequently encountered in cargo delivery, such as damages, losses, and bus travel delays, can be addressed through the implementation of monitoring systems. Damages and losses during cargo transportation, coupled with significant bus travel delays, stem from finalizing cargo delivery data after passenger check-in. This research aims to identify the root causes of these three issues and offer solution recommendations to enhance the reliability of the cargo delivery process. The research methodology includes analysis of historical delivery data, interviews with operational crews, and the development of a web-based information system for more structured monitoring processes. Research findings indicate that discrepancies between delivery schedules and inconsistent monitoring are primary factors contributing to damages, losses, and delays. Recommended solutions involve implementing real-time tracking systems and providing training to enhance awareness of swift and accurate delivery procedures. In conclusion, understanding the challenges encountered in cargo delivery can contribute to developing better service solutions, thereby improving overall logistics efficiency within DAMRI logistics.

Heterogeneous Traffic Flow Signal Control and CAV Trajectory Optimization Based on Pre-Signal Lights and Dedicated CAV Lanes

This paper proposes a control system to address the efficiency and pollutant emissions of heterogeneous traffic flow composed of human-operated vehicles (HVs) and connected and automated vehicles (CAVs). Based on the comprehensive collection of information on the flow of heterogeneous traffic, the control system uses a two-layer optimization model for signal duration calculation and CAV trajectory planning. The upper model optimizes the phase duration in real time based on the actual total number and type of vehicles entering the control adjustment zone, while the lower model optimizes CAV lane-changing strategies and vehicle acceleration optimization curves based on the phase duration optimized by the upper model. The target function accounts for reducing fuel usage, carbon emission lane-changing costs, and vehicle travel delays. Based on the Webster optimal cycle formula, an improved cuckoo algorithm with strong search performance is created to solve the model. The numerical data confirmed the benefits of the suggested signal control and CAV trajectory optimization method based on pre-signal lights and dedicated CAV lanes for heterogeneous traffic flow. Intersection capacity was significantly enhanced, CAV average fuel consumption, carbon emission and lane-changing frequency were significantly reduced, and traffic flow speed and delay were significantly improved.

Metro stop-skipping rescheduling optimization considering passenger waiting time fairness

ABSTRACT In urban metro systems, unexpected events often occur to cause discordance in the planned timetable and delay in passenger travel. Therefore, this study focuses on the timetable rescheduling problem by adopting a stop-skipping strategy during the off-peak period. In addition, there are a number of existing optimization models that minimize the total passenger waiting time, which can cause fairness issues for a small group of passengers having longer waiting time at some stations. In this study, a rescheduling model is proposed to consider passenger waiting time fairness and train travel time cost. This model is tested with a randomly designed delay scenario of Nanjing Metro Line 2, which can be efficiently solved by a designed iterative algorithm based on decomposition method. Compared to the standard stop pattern, the result shows that the stop-skipping pattern can better alleviate the impact of sudden delays, while simultaneously ensuring the passenger waiting time fairness.

On demand ride sharing: Scheduling of an autonomous bus fleet for last mile travel

Autonomous buses are expected to expand the mobility-on-demand options in cities in the next 10 years. An essential aspect of ensuring optimal usage of fleets of autonomous buses is the task of scheduling. This paper presents a scheduling approach based on the construction of all possible trips used to formulate an optimization problem. While in the past most research is focused on the scheduling of taxi trips, there is an increasing interest in the research for the scheduling of last-mile travel options. A street network has been generated based on open street map data as a basis for the scheduling. All possible combinations of buses and requests are calculated, and for each of those trips, a close to optimal order of requests is created. These are used to formulate the optimization problem, calculating a close to optimal assignment of requests on the buses. The approach has considered constraints such as maximum waiting time, travel delay, and targets to exploit shared trips for higher efficiency. Experiments have been carried out in a simulated environment of a university campus area with fleets of up to 10 vehicles. By performing various trials with changing parameters, the influence of the constraints on waiting time and travel delay to the scheduling is determined. Depending on the setup, service rates above 90%, while trails with strict constraints show that the approach can handle short-term requests. Based on the results, a use case-specific composition of the autonomous bus fleet can be done.

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks.

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization

In urban settings, the prevalence of traffic lights often leads to fluctuations in traffic patterns and increased energy utilization among vehicles. Recognizing this challenge, this research addresses the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs) through the introduction of a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD). This innovative strategy is designed to enhance various aspects of urban mobility, including vehicle energy efficiency, traffic flow optimization, and battery longevity, all while ensuring a satisfactory driving experience. The M-EAD strategy unfolds in two distinct stages: First, it optimizes eco-friendly green signal windows at traffic lights, with a primary focus on minimizing travel delays by solving the shortest path problem. Subsequently, it employs a receding horizon framework and leverages an iterative dynamic programming algorithm to refine speed trajectories. The overarching objective is to curtail energy consumption and reduce battery wear by identifying the optimal speed trajectory for EVs in urban environments. Furthermore, the research substantiates the real-world efficacy of this approach through on-road vehicle tests, attesting to its viability and practicality in actual road scenarios. In the proposed case, the simulation results showcase notable achievements, with energy consumption reduced by 0.92% and battery wear minimized to a mere 0.0017%. This research, driven by the pressing issue of urban traffic energy efficiency, not only presents a solution in the form of the M-EAD strategy but also contributes to the fields of sustainable urban mobility and EV performance optimization. By tackling the challenges posed by traffic lights, this work offers valuable insights and practical implications for improving the sustainability and efficiency of urban transportation systems.

Coordinated traffic control of urban networks with dynamic entrance holding for mixed CAV traffic

Traffic congestion has been regarded as one of the most challenging problems in the urban transportation systems. The problem becomes trickier at traffic bottlenecks where the heavy traffic flow spills over and over-saturation appears, and the coordination of traffic control strategies in a network scale is required. With the information availability and control precision enabled by connected and automated vehicle (CAV) technologies, this study proposes a network-level traffic coordination method with dynamic entrance holding in a mixed CAV traffic, and establishes a two-layer framework to develop traffic control strategies to handle the over-saturation problem. In the upper layer, the coordination of signal control among intersections is optimized to minimize travel cost within the network. The upper bound of travel delays under the control strategy is theoretically derived and the critical traffic volume is specified for designing the entrance holding strategy. To accelerate the solving process, a two-stage solution method along with a series of valid inequations are developed, and the feasibility and optimality of the solution are investigated. In the lower layer, a dynamic entrance holding strategy is put forward to regulate the entering traffic at network perimeters to avoid over-saturation and improve traffic orderliness. To enhance the holding effectiveness, a traffic prediction method is created to capture the future traffic evolution based on the real-time information, and combined with the flow reservation mechanism to proactively adjust the holding strategy in real time. Simulation experiments are conducted to evaluate the performance of the proposed methodologies, and compare with other control strategies. The results show that the coordinated method could substantially improve traffic mobility within the network, especially in high traffic demands.

A GIS Approach to Measuring Public Transport Travel Delay on Higher Order Roads in the City of Cape Town

Abstract. On a daily basis, hundreds of thousands of people in the City of Cape Town rely on road-based public transport as a means of commuting. A major factor that influences the passengers’ experience is the travel time to reach a desired destination. Due to various physical and operational factors, some sections of the road network experience high congestion and travel time delays during the morning and afternoon peak periods. Quantifying these delays, and the number of road-based public transport passengers exposed to the delay along a specific road section during a peak period allows informed decision-making and prioritisation. GIS and spatial data analysis are powerful tools to determine where and when such delays occur. Various spatial data sets exist that were jointly analysed to quantify the delay and the passenger volumes exposed to the delay on the city’s public transport network.

Investigating Routing in the VANET Network: Review and Classification of Approaches

Vehicular Ad Hoc Network (VANETs) need methods to control traffic caused by a high volume of traffic during day and night, the interaction of vehicles, and pedestrians, vehicle collisions, increasing travel delays, and energy issues. Routing is one of the most critical problems in VANET. One of the machine learning categories is reinforcement learning (RL), which uses RL algorithms to find a more optimal path. According to the feedback they get from the environment, these methods can affect the system through learning from previous actions and reactions. This paper provides a comprehensive review of various methods such as reinforcement learning, deep reinforcement learning, and fuzzy learning in the traffic network, to obtain the best method for finding optimal routing in the VANET network. In fact, this paper deals with the advantages, disadvantages and performance of the methods introduced. Finally, we categorize the investigated methods and suggest the proper performance of each of them.

Research on Passenger Flow Control Plans for a Metro Station Based on Social Force Model

To better utilise the service capacity of the limited facilities of a metro station, as well as ensure safety and transport efficiency during peak hours, a large passenger flow control plan is studied through theoretical analysis and numerical simulation. Firstly, by passenger data collection and data analysis, the characteristics of the inbound and outbound passenger flow of a T metro station are analysed. Secondly, AnyLogic evacuation simulation models for the T Station during peak hours, peak hours without/with passenger flow control are established based on real passenger flow data as well as the station structures and layouts by using the AnyLogic software. The results show that there are no obvious congestions in the station hall, and the travel delay is significantly reduced when effective passenger flow control measures are taken. By controlling the speed, direction and movement path of passengers, as well as adjusting the operation of escalators, entrances and automatic ticket-checking machines, passenger flow can become more orderly, transport efficiency can also be improved, and congestion in the station can be well mitigated.

Dynamic equilibrium analyses in a ride-sourcing market under travel time uncertainty

This paper proposes a dynamic equilibrium framework for the morning commute problem in the ride-sourcing market under travel time uncertainty. It is formulated as a simultaneous departure time and mode choice problem (SDTMCP) with random travel delay. Commuters are considered to choose among four travel modes: (1) driving alone, (2) taking non-shared service, (3) taking ride-sharing service, and (4) taking the metro. The equilibrium state is reached when no commuter can reduce their expected travel cost by unilaterally changing their mode or departure time. We derive the dynamic equilibrium queuing patterns analytically under certain assumptions, and develop an approach using nonlinear complementarity problem (NCP) to solve general problems where analytical results cannot be readily derived. We further explore the pricing strategy of the ride-sourcing platform, in which travel time uncertainty and the prices of ride-sourcing services jointly determine the profit performance. A mathematical program with equilibrium constraints (MPEC) is formulated to search for the optimal service prices to achieve the maximum profit. The framework is then extended to investigate the pricing strategy in a two-sided ride-sourcing market and examine the metro crowdedness effect in the numerical studies.

The effect of adopting the Next Generation Air Transportation System (NextGen) on air travel performance

The US Federal Aviation Administration (FAA) has implemented a large-scale multi-year infrastructure program called the Next Generation Air Transportation System (NextGen) to improve air transportation efficiency. To assess its efficacy, we estimate how NextGen projects completed between 2014 and 2017 affected air travel time and delays using an event study approach. We find sizable time savings in air travel time and delays from implementing NextGen. The time savings are more substantial for flights that experience unexpected shocks, such as poor weather and prior delays. In contrast, while NextGen seemed to close the performance gap between the hub and non-hub carriers generated by market power, the effect was short-lived and quickly reversed. Although we also find some small social benefits from carbon emission reductions, we cannot rule out that aggregate carbon emissions may have increased due to a rebound effect.

Coordinating CAV Swarms at Intersections With a Deep Learning Model

Connected and automated vehicles (CAVs) have the potential to significantly improve the safety and efficiency of traffic. One revolutionary CAV’s impact on transportation system is cooperative driving that turns signalized intersections to be signal-free and boosts traffic efficiency by better organizing the passing order of CAVs. However, how to get the optimal passing order is an NP-hard problem (specifically, enumerating based algorithm takes days to find the optimal solution to a 20-CAV scenario). Here, we introduce a novel cooperative driving algorithm (AlphaOrder) that combines offline deep learning and online tree searching to find a near-optimal passing order in real-time. AlphaOrder builds a pointer network model from solved scenarios and generates near-optimal passing orders instantaneously for new scenarios. For the scenarios with 40 CAVs, AlphaOrder reduces the travel delay by more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20\%$</tex-math> </inline-formula> on average compared to the best-so-far MCTS based algorithm. Moreover, our algorithm provides a general approach to managing preemptive resource sharing between multi-agents (e.g., scheduling multiple automated guided vehicles (AGVs) and unmanned aerial vehicles (UAVs) at conflicting areas).

Resilience as a Service for Transportation Networks: Definition and Basic Concepts

Urban transportation systems' structure and functionality can be affected by unexpected disruptions for several reasons, such as natural hazards, intentional attacks, accidents, and so forth. The conventional definition of resilience is the capacity to withstand, assimilate, adjust, and expeditiously recuperate from various forms of perturbations such as shocks, disturbances, and deliberate attacks. Though multiple studies in the literature focus on resilience assessment and improving the resilience level of mobility services before disruption, few studies offer solutions for the operators of transportation systems during disruptions to alleviate their negative effects, such as reducing the recovery time. In this context, a new paradigm called ``resilience as a service'' (RaaS) has emerged in the field of operations management. The idea of RaaS is to integrate the available resources of different service providers to manage disruptions and maintain the system's resilience. This paper proposes a definition of RaaS dedicated to transportation systems. To provide a methodological example for the RaaS paradigm, we formulate a bi-level optimization problem to represent a solution example that RaaS providers can deliver. The upper-level model formulates the resource reallocation problem during disruption from the perspective of RaaS providers, while the lower-level model considers user perspectives. We provide a numerical example in a real test case of a French city to illustrate the benefits of implementing a RaaS solution. The results show that we can reduce the average travel delay of all users by 69%, including the delay results from the proposed RaaS strategy compared with the absence of RaaS.

A Signal Optimization Model of Adjacent Closely Spaced Intersections Which Optimizes Pedestrian Crossing

Adjacent closely spaced intersections with characteristics of short link distance and high pedestrian flow are primarily located in high-density urban areas. To address the problems of queue overflow, poor traffic operation, and high pedestrian travel delays, a pedestrian-motor vehicle signal optimization method for adjacent closely spaced intersections was proposed in this paper. First, the traffic flow entering the closely spaced intersections is divided into nonarterial and arterial flow categories to establish a delay model of pedestrian crossing. Then, a pedestrian crossing delay model based on pedestrian demand is constructed according to pedestrian crossing time and a space diagram. An optimization model for pedestrians and vehicles at adjacent closely spaced intersections is established, and an artificial intelligence algorithm is used to optimize this model. Finally, a selected case intersection is optimized. The results show that compared with a traditional single optimization method, vehicle delay decreased about 4%, 13.8%, 17.1%, and 25.9% and total pedestrian delay decreased by 3%, 15%, 25%, and 31%, respectively, for the four proposed scenarios.

Comparison of Flow- and Bandwidth-Based Methods of Traffic Signal Offset Optimization

Methods of optimizing offsets in coordinated traffic signal systems can be grouped into two categories: bandwidth based and flow based (e.g., minimizing delay). To our knowledge, no studies have directly compared methods from both categories. This study fills this gap with quantitative comparison between flow- and bandwidth-based methods. This paper evaluates the performance of 11 methods of offset optimization, including several different objectives that make use of arrival profiles (e.g., maximizing arrivals on green, minimizing delay, minimizing the number of stops) and several different methods of bandwidth maximization. These are compared against a default scenario with all offsets set to zero, and a manual method for determining offsets. An eight-intersection simulation network is used for comparison. The outcomes are compared in terms of the effective bandwidth, delay and stops, arrivals on green, and travel time. The results show that flow-based methods that incorporate models of delay and/or stops tend to yield more directionally balanced travel delays as well as lower total arterial delay and stops. Bandwidth-based methods achieved higher bandwidth and arrivals on green. Some additional comparisons are made among the 11 tested methods.