Reducing CO2 emissions by improving road design: a driving simulator study

Gaetano Bosurgi — 2025-02-14

In the last decade, the causes of Greenhouse Gas (GHG) emissions were widely studied, to delete or, at least, mitigate them. In the road context, as reasonable, greater importance was assigned to the vehicles, since huge traffic flows, including high percentages of trucks, determine negative impacts on the environment. On the contrary, the role of the road infrastructure has always been considered marginal. It was thought as a functional element on which the traffic flows move, without evaluating the role of its geometrical characteristics on exhaust gas emissions. The proposed research aims to verify whether some road features, related to its horizontal geometry, influence the carbon dioxide production of vehicles or, on the contrary, if it is not sensitive to the different geometrical compositions. A driving simulator gives the opportunity to calculate the emissions from fuel consumption data, in turn, calculated through the engine mapping of an ordinary vehicle. The proposed procedure may be easily applied to any road context and may represent a further checking element for the infrastructure efficiency, in terms of environmental impacts. The results, derived from a test phase in a simulated environment and obtained using 3 different one-way ANOVAs, allowed the authors to define some interesting conclusions. The trend of the carbon dioxide function depends on curve radius and lengths and on tangent length; therefore, an opportune alignment design can effectively contribute to control emission values. The experiments confirmed that designing a consistent road is fundamental, but this cannot be deduced by traditional literature models.

Forecasting short-term passenger flow on a bus route: a splitting–integrating method based on passenger travel behavior

Xiaoping Fang — 2025-02-24

Short-term passenger flow forecasting is the key to implement real-time dynamic dispatching of buses, which can meet the travel time requirement of passengers with different attributes. In practice, it is difficult to obtain passenger attribute information due to the restriction of bus information systems or other conditions. This article proposes a new perspective on identifying passenger attribute information, that is, the correlation between the bus card number and the travel time is used to analyse passenger travel behaviour. Then using the travel frequency as the splitting boundary, the passenger set is split into different types of subsets, which are predicted by different methods. The total forecast values are obtained by integration, so as to explore the effectiveness of the passenger attribute identification and splitting–integrating method. The result shows that: (1) compared with the forecasting method without considering the passenger travel behaviour, the performance of splitting–integrating method is better, and the passenger attribute identification method is effective; (2) the value of the splitting boundary will affect the size and consistency of the subset, and the optimal value can be sought according to forecast results; (3) different types of subsets should be treated by different forecasting models and combination paths.

Transport

Reducing CO2 emissions by improving road design: a driving simulator study

Forecasting short-term passenger flow on a bus route: a splitting–integrating method based on passenger travel behavior