Urban air quality is generally poor at traffic intersections due to variations in vehicles’ speeds as they approach and leave This paper examines the effect of traffic, vehicle and road characteristics on vehicular emissions with a view to understand a link between emissions and the most likely influencing and measurable characteristics It demonstrates the relationships of traffic, vehicle and intersection characteristics with vehicular exhaust emissions and reviews the traffic flow and emission models Most studies have found that vehicular exhaust emissions near traffic intersections are largely dependent on fleet speed, deceleration speed, queuing time in idle mode with a red signal time, acceleration speed, queue length, traffic-flow rate and ambient conditions The vehicular composition also affects emissions These parameters can be quantified and incorporated into the emission models There is no validated methodology to quantify some non-measurable parameters such as driving behaviour, pedestrian activity, and road conditions

Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections

One way to reduce excessive fuel consumption and vehicular emissions on urban streets is to optimize signal timings. Historically, signal timing optimization tools were used to reduce traffic delay and stops. The concept of optimizing signal timings to reduce fuel consumption and emissions was addressed decades ago with tools that are now considered outdated. This study advocates a fresh approach to integrating existing state-of-the-art tools for reassessing and ultimately minimizing fuel consumption and emissions. VISSIM, CMEM, and VISGAOST were linked to optimize signal timings and minimize fuel consumption and CO2 emissions. As a case study, a 14-intersection network in Park City, Utah, was used. Signal timings were optimized for seven optimization objective functions to find the lowest fuel consumption and CO2 emissions. Findings show that a formula commonly used to estimate fuel consumption in traffic simulation tools inadequately estimates fuel consumption and cannot be used as a reliable objective ...

Optimizing traffic control to reduce fuel consumption and vehicular emissions: Integrated approach with VISSIM, CMEM, and VISGAOST

Traffic signals typically introduce vehicle stops along a trip and thus increase vehicle fuel consumption levels. In attempt to enhance vehicle fuel efficiency, eco-cooperative adaptive cruise control (Eco-CACC) systems are being developed that receive signal phasing and timing data from downstream signalized intersections via vehicle-to-infrastructure communication. In this paper, an Eco-CACC algorithm is developed that computes the fuel-optimum vehicle trajectory through a signalized intersection by ensuring that the vehicle arrives at the intersection stop bar just as the last queued vehicle is discharged. A simulation analysis demonstrates that the proposed Eco-CACC system produces vehicle fuel savings up to 40%, when the market penetration rate (MPR) is 100%. The results also demonstrate that for single-lane approaches, the algorithm reduces the overall fuel consumption levels for all vehicles, and that higher MPRs result in larger savings. Alternatively, on multi-lane approaches, lower MPRs produce negative impacts on the overall intersection fuel efficiency, and only when MPRs are greater than 30% does the algorithm produce overall intersection fuel consumption savings.

Eco-Cooperative Adaptive Cruise Control at Signalized Intersections Considering Queue Effects

Vehicle fuel consumption and emissions are directly related to the acceleration/deceleration patterns and the idling period. In order to reduce emissions and improve fuel economy, sharp acceleration/deceleration and idling should be avoided as much as possible. Unlike on freeways, traffic on signalized corridors suffers from increased fuel consumption and emissions due to idling and acceleration/deceleration maneuvers at traffic signals. By taking advantage of the recent developments in communication technology between vehicles and roadside infrastructure, it is possible for vehicles to receive the signal phase and timing information well in advance of approaching a signalized intersection. Based on this traffic signal information, we have developed arterial velocity planning algorithms that give dynamic speed advice to the driver so that the probability of having a green light is maximized when approaching signalized intersections. The algorithms are aimed at minimizing the acceleration/deceleration rates while ensuring that the vehicle never exceeds the speed limit, and that it will pass through intersections without coming to a stop. Using a stochastic simulation technique, the algorithms are used to generate sample vehicle velocity profiles along a 10-intersection signalized corridor. The resulting vehicle fuel consumption and emissions from these velocity profiles are calculated using a modal emissions model, and then compared with those from a typical velocity profile of vehicles without velocity planning. The energy/emission savings for vehicles with velocity planning are found to be 12–14%.

Arterial velocity planning based on traffic signal information under light traffic conditions

There are a variety of strategies that are now being considered to reduce fuel consumption and carbon dioxide (CO 2 ) emissions from the transportation sector. One strategy that is gaining interest worldwide is known as “eco-driving”. Eco-driving typically consists of changing a person's driving behavior based on general (static) advice to the driver, such as accelerating slowly, driving smoothly, reducing high speeds, etc. Taking this one-step further, it is possible to provide realtime advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings. This concept of dynamic eco-driving takes advantage of real-time traffic sensing and infrastructure information, which can then be communicated to a vehicle with a goal of reducing fuel consumption and emissions. In this paper, we consider dynamic eco-driving in an arterial corridor with traffic signals, where signal phase and timing information of a traffic light is provided to the vehicle. The vehicle can then adjust its velocity while traveling through a signalized corridor with the goal of minimizing fuel consumption and emissions. A dynamic eco-driving velocity planning algorithm has been developed and is described herein. This algorithm has then been tested in simulation, showing initial fuel economy and CO 2 improvements of around 12%.

Dynamic ECO-driving for arterial corridors

In the proposed signal timing model, a performance index function for optimization is defined to reduce vehicle delays, fuel consumption and emissions at intersections. The model optimizes the signal cycle length and green time by considering the constraint of a minimum green time to allow pedestrians to cross. The data used in a case study is from an intersection in Nanjing city. The relationships between the signal cycle length and vehicle delay, fuel consumption, emission, and performance index function are analyzed.

Jialin Tian

Papers

Signal timing of intersections using integrated optimization of traffic quality, emissions and fuel consumption: a note