Saniul Alam

Cooperative and connected autonomous vehicles (CCAVs) are considered to be a promising solution for addressing congestion and other operational deficiencies, as part of a holistic future mobility management framework. As a result, a significant number of studies have recently been published on this topic. From the perspective of future mobility management, this review paper discusses three themes, which are traffic management, network performance, and mobility management, including congestion, and incident detection using the PRISMA methodology. Three databases were considered for this study, and peer-reviewed primary studies were selected that were published within the last 10 years in the English language, focusing on CCAV in the context of the future transportation and mobility management perspective. For synthesis and interpretation, like-for-like comparisons were made among studies; it was found that extensive research-supported information is required to ensure a smooth transition from conventional vehicles to the CCAVs regime, to achieve the projected traffic and environmental benefits. Research investigations are ongoing to optimize these benefits and associated goals via the setting of different models and simulations. The tools and technologies for the testing and simulation of CCAV were found to have limited capacity. Following the review of the current state-of-the-art, recommendations for future research have been discussed. The most notable is the need for large-scale simulations to understand the impact of CCAVs beyond corridor-based and small-scale networks, the need for understanding the interactions between the drivers of CCAVs and traffic management centers, and the need to assess the technological transition, as far as infrastructure systems are concerned, that is necessary for the progressive penetration of CCAVs into traffic streams.

The intrazonal modeling parameters of distance, duration, and generalized cost (GC) are conventionally estimated from a fixed-fraction of interzonal parameters or using linear equations. In this study, the intrazonal GC that was developed in the National Transport Authority’s (NTA) regional modeling system using a conventional fixed-fraction method (base) was compared to intrazonal GCs for six travel modes [passenger cars, bus, cycle, walk, taxi (passenger) and light goods vehicles] that were estimated using zonal GC equations adopted from literature. The zone-specific distance and duration values for the parameters of the six equations were developed using a regression modeling approach. At first, National Household Travel Survey (NHTS) data, Open Street Map, and traffic zone data were used to generate regression models. Then the reported trip duration and distance of NHTS datasets were then replaced with the corresponding time and coordinate defined distances and durations from the Google Maps Journey Time and Distance Estimates (GMJTDE) records, and the estimated equations from this process produced better validation performance, and thus integrated with the six equations. In the comparison stage, intrazonal GC values were estimated for all six modes using two different analysis and compared against the base dataset. In the first analysis, average distances and duration were obtained for all zones containing intrazonal trips and were directly applied in the six equations to estimate intrazonal GC. In the second analysis, intrazonal GC were estimated using models based on the aforementioned NHTS-GMJTDE datasets. Results indicate that the intrazonal GC estimated using the second analysis matched the intrazonal GC from the conventional method, and provided an indication of real-world situations to some extent. This study confirms that intrazonal parameters can be developed separately from the survey data, minimizing the efforts of calibration with sufficient accuracy that cannot be obtained directly from the survey data.

Traffic is a major source of urban air pollution that affects health, especially among children. As lower speed limits are commonly applied near schools in many cities, and different governments have different policies on vehicle fleet composition, this research estimated how different speed limits and fleet emissions affect air quality near a primary school. Based on data of traffic, weather, and background air quality records in Dublin from 2013, traffic, emission, and dispersion models were developed to assess the impact of different speed limits and fleet composition changes against current conditions. Outside the school, hypothetical speed limit changes from 30 km/h to 50 km/h could reduce the concentration of NO₂ and PM₁₀ by 3% and 2%; shifts in the fleet from diesel to petrol vehicles could reduce these pollutants by 4% and 3% but would increase the traffic-induced concentrations of CO and Benzene by 63% and 35%. These changes had significantly larger impacts on air quality on streets with higher pollutant concentrations. Findings suggest that both road safety and air quality should be considered when determining speed limits. Furthermore, fleet composition has different impacts on different pollutants and there are no clear benefits associated with incentivising either diesel or petrol engine vehicles.

Eco-Driving, a driver behaviour-based method, has featured in a number of national policy documents as part of CO₂ emission reduction or climate change strategies. This investigation comprises a detailed assessment of acceleration and deceleration in Eco-Driving Vehicles at different penetration levels in the vehicle fleet, under varying traffic composition and volume. The impacts of Eco-Driving on network-wide traffic and environmental performance at a number of speed-restricted road networks (30 km/h) is quantified using microsimulation. The results show that increasing levels of Eco-Driving in certain road networks result in significant environmental and traffic congestion detriments at the road network level in the presence of heavy traffic. Increases in CO₂ emissions of up to 18% were found. However, with the addition of vehicle-to-vehicle or vehicle-to-infrastructure communication technology which facilitates dynamic driving control on speed and acceleration/deceleration in vehicles, improvements in CO₂ emissions and traffic congestion are possible using Eco-Driving.

Particulate matter (PM) is one of the most problematic air pollutants in Ireland, and recently the associations between exposure to ambient PM and...