Alternate Ramp Merging Strategy for Connected Vehicles Based on Roadside Unit Support

With the emergence of connected vehicle technologies, the potential positive impact of connected vehicle guidance on mobility has become a research hotspot by data exchange among vehicles, infrastructure, and mobile devices. This study is focused on micro-modeling and quantitatively evaluating the impact of connected vehicle guidance on network-wide travel time by introducing various affecting factors. To evaluate the benefits of connected vehicle guidance, a simulation architecture based on one engine is proposed representing the connected vehicle–enabled virtual world, and connected vehicle route guidance scenario is established through the development of communication agent and intelligent transportation systems agents using connected vehicle application programming interface considering the communication properties, such as path loss and transmission power. The impact of connected vehicle guidance on network-wide travel time is analyzed by comparing with non-connected vehicle guidance in response to different market penetration rate, following rate, and congestion level. The simulation results explore that average network-wide travel time in connected vehicle guidance shows a significant reduction versus that in non–connected vehicle guidance. Average network-wide travel time in connected vehicle guidance have an increase of 42.23% comparing to that in non-connected vehicle guidance, and average travel time variability (represented by the coefficient of variance) increases as the travel time increases. Other vital findings include that higher penetration rate and following rate generate bigger savings of average network-wide travel time. The savings of average network-wide travel time increase from 17% to 38% according to different congestion levels, and savings of average travel time in more serious congestion have a more obvious improvement for the same penetration rate or following rate.

Traffic congestion and delays at signalized intersections remain a challenge in urban mobility. To minimize congestion, adaptive traffic signal control systems have been adopted across the United States. However, for these systems to function at their best, systems require an accurate estimation of traffic states, such as traffic density and queue length. This paper implements a relatively novel method, Physics-Informed Neural Networks (PINNs), for real-time traffic density estimation to enhance adaptive intersection signal control. The methodology leverages PINNs, which integrate both data-driven learning and physical constraints imposed by the Lighthill-Whitham-Richards (LWR) macroscopic traffic flow model. A simulation of a four-way intersection was used to generate realistic spatio-temporal traffic data to evaluate the performance of the model. Separate PINNs models, one trained for each incoming approach using simulation-based spatio-temporal density data, provide segment-level density predictions. These real-time density estimates are then employed within an adaptive traffic signal control algorithm designed to adjust green light phasing based on detected demand. The proposed PINNs control strategy was evaluated through simulation, comparing its performance against the Webster traffic timing method using key metrics such as average vehicle delay at the intersection and average vehicle travel time. The findings show an approximate 18% decrease in average vehicle delay and more than a 6% reduction in average travel time when utilizing adaptive traffic control based on the PINNs method for determining density values. These findings highlight the potential of leveraging PINNs for accurate, real-time traffic state estimation, which can effectively inform and improve the performance of adaptive traffic signal control systems, leading to reduced congestion and enhanced mobility at intersections.

Giving right of way green signal for the Emergency Service Vehicles (ESV) like ambulance and fire service rescue vehicle with minimum delay is a major challenge in Intelligent Transportation Systems (ITS). This challenge is currently achieved by various conventional Emergency Vehicle Pre-emption (EVP) Systems. These conventional systems fail miserably due to false triggering, line of sight issues, network delays, high cost and low reliability. In this paper, we propose a new decentralized Radio Frequency (RF) and Global Navigation Satellite System (GNSS) based Geo-fencing approach which allows smooth passage of ESVs through the junctions with minimum delays. The proposed system was field trailed in one of the junctions in Thiruvananthapuram, India. Pre and post-implementation travel time of emergency vehicles at the junction was evaluated by an external agency National Transportation Planning and Research Centre (NATPAC), Thiruvananthapuram. The evaluation result shows that there is a considerable reduction in the average travel time taken by the emergency vehicles to cross the cordon area in the post-implementation stage as compared to the pre-implementation stage. The reduction in average travel time is approximately 48%.

We enhance the operational performance of a textile factory by optimizing its production layout. The optimized layout improves operational time and increases overall productivity through strategic layout planning and workflow adjustments. The systematic layout planning method, combined with the analytical hierarchy process and technique for order of preference by similarity to an ideal solution from the multi-criteria decision-making process, is used to develop and select the optimal layout solution. The results show that the implementation of the improved layout design in alternative 1 led to a 45% reduction in average travel time and a 34% reduction in travel distance. This proposed solution optimizes production space while improving flexibility, safety, and competitiveness.

Leveraging large language models (LLMs) in traffic signal control (TSC) improves optimization efficiency and interpretability compared to traditional reinforcement learning (RL) methods. However, existing LLM-based approaches are limited by fixed time signal durations and are prone to hallucination errors, while RL methods lack robustness in signal timing decisions and suffer from poor generalization. To address these challenges, this paper proposes HeraldLight, a dual LLMs architecture enhanced by Herald guided prompts. The Herald Module extracts contextual information and forecasts queue lengths for each traffic phase based on real-time conditions. The first LLM, LLM-Agent, uses these forecasts to make fine grained traffic signal control, while the second LLM, LLM-Critic, refines LLM-Agent's outputs, correcting errors and hallucinations. These refined outputs are used for score-based fine-tuning to improve accuracy and robustness. Simulation experiments using CityFlow on real world datasets covering 224 intersections in Jinan (12), Hangzhou (16), and New York (196) demonstrate that HeraldLight outperforms state of the art baselines, achieving a 20.03% reduction in average travel time across all scenarios and a 10.74% reduction in average queue length on the Jinan and Hangzhou scenarios. The source code is available on GitHub: https://github.com/BUPT-ANTlab/HeraldLight.

Considering mixed-autonomy traffic with connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs), a CAV cooperative control strategy was proposed to improve the traffic flow efficiency in freeway ramp merging areas. The roadside unit controlled the movements of CAVs both on the ramp and on the mainline before the merging point, according to real-time traffic conditions. Microscopic simulations based on cellular automata were performed to evaluate the influences of the proposed cooperative control strategy on the average vehicle delays of the mainline, the ramp, and the entire merging area. Sensitivity analyses were performed with varying CAV market penetration rates and traffic demand levels. Results show that, compared to no-control case, the proposed cooperative control strategy may effectively reduce the average vehicle delay of the mainline. Though the average vehicle delay of the ramp increases, the average vehicle delay of entire merging area is still improved, because the mainline flow volume is usually much larger than the ramp flow volume. Furthermore, the proposed cooperative control strategy only requires a limited number of control variables, making it feasible to determine the optimal control variables based on sensitivity analysis.

Dynamic Speed Guidance for CAV Ramp Merging in Non-Cooperative Environment: An On-Site Experiment

How to make vehicles run efficiently under safe conditions is one of important research topics in the field of transportation. The development of connected and autonomous vehicles (CAVs) could improve the driving efficiency with intelligence of CAVs. In this study, we firstly analyze several typical vehicles merging scenes, and establish driving models during the merging process. Then, this paper takes a typical on-ramp merging scene as an example, and proposes a vehicle merging strategy based on improved cooperative game theory. Finally, by quantifying the safety and efficiency of vehicle traffic, and assigning them different weights, simulation experiments verify the effectiveness of a vehicle merging strategy in improving traffic efficiency. Taking the trajectory of the vehicles and the average travel time of all vehicles as the output result, comparing with the vehicle merging model controlled by the fixed timing of traffic lights, the result shows that the vehicle merging strategy based on improved cooperative game theory can significantly improve traffic efficiency. Especially, under the test of different traffic flows, the average travel time of all vehicles increased by 10.95%. The merging strategy based on improved cooperative game theory can contribute to solve traffic conflict in the future.

Ramp merging is considered to be one of the major causes of traffic accidents and congestion due to its inherent chaotic nature. With the development of the connected and automated vehicle (CAV) technology, CAVs can conduct cooperative merging using communication, and can also handle complicated situations even with legacy vehicles. In this article, a game theory-based ramp merging strategy has been developed for the optimal merging coordination of CAVs in mixed traffic, which can determine the dynamic merging sequence and corresponding longitudinal/lateral control. This strategy improves the safety and efficiency of the merging process by ensuring a safe intervehicle distance and harmonizing the speeds of CAVs in the traffic stream. To verify the proposed strategy, mixed traffic simulation runs under different penetration rates and different congestion levels have been carried out on an innovative Unity-SUMO integrated platform, which connects a game engine-based driving simulator with a state-of-the-art microscopic traffic simulator. The results show that the average speed of traffic flow can be increased up to 210%, while the fuel consumption can be reduced up to 53.9%. In addition, the driving volatility can be stabilized to a level with 0% extreme values.

Provision of public transport with autonomous vehicles may enable the implementation of more flexible services, characterised by a reduction of the vehicle's capacity (and increase in the number of vehicles) — leading to fewer stops, better frequencies, and more individualised services (origins and destinations). If public transport provision is centralised, increasing flexibility will lead to reductions in average travel times (although not to the optimal capacity), as the provider internalises congestion costs; however, if public transport is provided in competitive conditions, it may lead to situations in which the entire society ends up worse off, representing a clear transportation paradox.

The location of a facility can be examined in the context of several classical frameworks in economic geography and operation research where the facility is located such that some objective function is minimised (ie. Love et al., 1988; Ghosh and McLafferty, 1987; Wrigley, 1988; Fotheringham and O’Kelly, 1989). A frequently encountered objective, for example, is to find the optimal location of a new facility in terms of minimising the average time it takes individuals to travel to the nearest facility. That is, a new facility is added to an existing spatial distribution of facilities in order to achieve the maximum reduction in average travel times (the p-median problem). Other objective functions can of course be used to locate the new facility: it could for example, be located so that the maximum distance any individual has to travel is minimised (the minimax problem). Another slant on the problem is to simultaneously locate a set of facilities and to determine the allocation of demand to each of these facilities (the location-allocation problem). Still another is to model the choice of a facility by an individual as a probabilistic function of the attributes of each facility rather than as a deterministic one (a spatial interaction problem).

This study evaluates the potential of IoT-enabled adaptive traffic management systems for mitigating urban congestion, enhancing mobility, and reducing environmental impacts in densely populated cities. Using London as a case study, the research develops a multiagent simulation framework to assess the effectiveness of advanced traffic management strategies-including adaptive signal control and dynamic rerouting-under varied traffic scenarios. Unlike conventional models that rely on static or reactive approaches, this framework integrates real-time data from IoT-enabled sensors with predictive analytics to enable proactive adjustments to traffic flows. Distinctively, the study couples this integration with a multiagent simulation environment that models the traffic actors-private vehicles, buses, cyclists, and emergency services-as autonomous, behaviourally dynamic agents responding to real-time conditions. This enables a more nuanced, realistic, and scalable evaluation of urban mobility strategies. The simulation results indicate substantial performance gains, including a 30% reduction in average travel times, a 50% decrease in congestion at major intersections, and a 28% decline in CO2 emissions. These findings underscore the transformative potential of sensor-driven adaptive systems for advancing sustainable urban mobility. The study addresses critical gaps in the existing literature by focusing on scalability, equity, and multimodal inclusivity, particularly through the prioritisation of high-occupancy and essential traffic. Furthermore, it highlights the pivotal role of IoT sensor networks in real-time traffic monitoring, control, and optimisation. By demonstrating a novel and practical application of sensor technologies to traffic systems, the proposed framework makes a significant and timely contribution to the field and offers actionable insights for smart city planning and transportation policy.

The increasing choice of public private partnerships (PPPs) in infrastructure projects is due to its inherent promise to create value for money (VfM) by providing timely and improved quality of services cost effectively. This article makes an attempt to assess the performance of four national highways (NH) PPP projects in the State of Karnataka. Framework for the evaluation of the projects under review includes time overrun analysis, risk responsibility matrix, and estimation of both quantitative and qualitative VfM, that is, the financial savings to the government and road users. The study finds that the Projects 1, 3, and 4 under review created positive VfM to government (financial savings) to the tune of Rs. 1,040 crore. VfM has also been observed to be positive to the road users by way of average reduction in travel time, saving of fuel, consistency in reaching the destination, and so on. The article also provides suggestions to tone up the PPP policy of India.

Author(s): Wu, Guoyuan; Zhao, Zhouqiao; Wang, Ziran; Barth, Matthew J. | Abstract: With on-board sensors such as camera, radar, and Lidar, connected and automated vehicles (CAVs) can sense the surrounding environment and be driven autonomously and safely by themselves without colliding into other objects on the road. CAVs are also able to communicate with each other and roadside infrastructure via vehicle-to-vehicle and vehicle-to-infrastructure communications, respectively, sharing information on the vehicles’ states, signal phase and timing (SPaT) information, enabling CAVs to make decisions in a collaborative manner. As a typical scenario, ramp control attracts wide attention due to the concerns of safety and mobility in the merging area. In particular, if the line-of-the-sight is blocked (because of grade separation), then neither mainline vehicles nor on-ramp vehicles may well adapt their own dynamics to perform smoothed merging maneuvers. This may lead to speed fluctuations or even shockwave propagating upstream traffic along the corridor, thus potentially increasing the traffic delays and excessive energy consumption. In this project, the research team proposed a hierarchical ramp merging system that not only allowed microscopic cooperative maneuvers for connected and automated electric vehicles on the ramp to merge into mainline traffic flow, but also had controllability of ramp inflow rate, which enabled macroscopic traffic flow control. A centralized optimal control-based approach was proposed to both smooth the merging flow and improve the system-wide mobility of the network. Linear quadratic trackers in both finite horizon and receding horizon forms were developed to solve the optimization problem in terms of path planning and sequence determination, and a microscopic electric vehicle (EV) energy consumption model was applied to estimate the energy consumption. The simulation results confirmed that under the regulated inflow rate, the proposed system was able to avoid potential traffic congestion and improve the mobility (in terms of average speed) as much as 115%, compared to the conventional ramp metering and the ramp without any control approach. Interestingly, for EVs (connected and automated EVs in this study), the improved mobility may not necessarily result in the reduction of energy consumption. The “sweet spot” of average speed ranges from 27–34 mph for the EV models in this study.View the NCST Project Webpage

Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and contribute to lower fuel economy, even increasing the collision risk. Cooperative control for connected and automated vehicles (CAVs) has the potential to significantly reduce the negative impact and improve safety and traffic efficiency. Implementation of cooperative on-ramp merging requires the assistance of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, wherein the communication delay may cause negative impact on CAV cooperative control. In this paper, scenario of on-ramp merging for CAVs considering the V2I communication delay are studied. Statistical characteristics of the V2I communication delay are explored from both literature and real field test, and a communication delay estimation model based on statistical techniques are proposed. Specifically, we firstly model the CAV on-ramp merging scenario using optimal control in ideal situation. Then, several statistical characteristics of the V2I communication are investigated especially the probability density function of the V2I communication delay in several application scenarios. Further, we proposed a communication delay estimation model and used the modified vehicle state to compute the corresponding control law. Real field test of V2I communication delay indicated that distribution of V2I communication delay could correlate with the application scenario and normal distribution can be generally adopted to approximate the probability density function (PDF) when the number of samples is large enough. Numerical simulation of the CAV on-ramp merging scenario considering the V2I communication delay revealed that dynamic performance of the control process would be deteriorated impacted by the V2I communication delay and it might further impact the final control effect and lead to potential lateral collision in the merging area.