Cooperative Intelligent Transport Systems services are designed to enhance road safety, traffic efficiency, and reduce vehicle impact on the environment. In this paper, we focus on evaluating the impact of C-ITS services, specifically, we evaluate the case of signaling road works zones on highways. We adopt a simulation-based evaluation methodology using the Simulation of Urban MObility (SUMO) traffic simulator. The novelty of this study lies in modeling response delays for drivers using C-ITS services within the simulation framework. The purpose is to mimic the behavior of a driver receiving a C-ITS notification with recommendation to anticipate a lane change while approaching work zones. The impact assessment was performed with a distinction between CVs (Connected Vehicles) and non-CVs. The study highlights that Road Works warnings with lane-change recommendations can be highly beneficial in low-traffic demand scenarios but should not be broadcast in high-traffic demand scenarios and should be replaced with more adequate messages without lane change recommendation, such as, traffic jam ahead warning.

What are the main factors related to road or service configuration influencing the response behaviour of connected vehicles? How does it evolve with respect to the Market Penetration Rate (MPR) of connected vehicles? Here are some questions raised by this paper with a focus made on Green Light Optimal Speed Advisory (GLOSA) strategy. Such a system, based on V2I communication, aims at providing speed advice/recommendations when approaching an intersection to adjust speed and enhance fuel consumption. The message is displayed on the Human Machine Interface (HMI) of the connected vehicles and a response is expected from the driver. This paper derives its interest in the response behaviour of the driver to HMI. It develops a two-stage methodology based on (i) Field Operational Test to collect realistic inputs (e.g., response rate, delay, deceleration profile, etc.) and (ii) a simulated environment used for extending the findings to non-observed cases (e.g. higher MPR). Besides, the methodology that is well-fitted for generic evaluation and comparison of pilots sites’ conclusions, one further contribution lies in the process to select the explaining factors. Factors are targeted among features of (i) the road configuration (e.g. number of lanes), (ii) the service configuration (e.g. activation distance), or (iii) the individual route choice and traffic conditions. Among others, it is highlighted that the activation distance plays a significant role in the response behaviour and, depending on the cycle duration, a short activation distance might be completely inefficient, while a true environmental impact requires high MPR.

The emergence of new technologies allows better monitoring of traffic conditions and understanding of urban network dynamics. Bluetooth technology is becoming widespread, as it represents a cost-effective means for capturing road traffic in both arterials and motorways. Although the extraction of travel time from Bluetooth data is fairly straightforward, data reliability and processing is still challenging with the issues of penetration rate, mode discrimination, and detection quality. This paper presents a methodological contribution to the use of Bluetooth data for the spatiotemporal analysis of a large urban network (Brisbane, Australia). It introduces the concept of the Bluetooth origin-destination (B-OD) matrix, which is built from a network of 79 Bluetooth detectors located within the Brisbane urban area. The B-OD matrix describes the dynamics of a subpopulation of vehicles, between pairs of detectors. The results show that the characteristics of urban networks can be effectively represented through B-OD matrices. A comparison with loop detector data enables an assessment of the results' significance. Then, the spatiotemporal structure of the network is analyzed with two different clustering analyses, namely, latent Dirichlet allocation (LDA) and $K$-means. While LDA is used to detect a temporal pattern, the $K$-means algorithm highlights Bluetooth fundamental diagram (BFD) classes. The results show that Bluetooth data has the potential to be a reliable data source for traffic monitoring. By highlighting hidden structures of a large area, the algorithm outputs allow us to provide the road operators with a fine spatiotemporal analysis of their network, in terms of traffic conditions.