Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p<0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p<0.05).

In this paper, we evaluate and improve the performance of the medium-access control (MAC) protocol for safety applications in a dedicated short-range communication (DSRC) environment. We first develop an analytical model to study the IEEE 802.11 distributed coordination function (DCF) MAC protocol that has been adopted by the IEEE 802.11p standard for DSRC. Explicit expressions are derived for the mean and standard deviation of the packet delay, as well as for the packet delivery ratio (PDR) at the MAC layer in an unsaturated network formed by moving vehicles on a highway. The proposed model is validated using extensive simulations, and its superior accuracy is compared with that of other existing models is demonstrated. Insights gained from our model reveal that the principal reason for the low PDR of the DCF protocol is packet collision due to transmissions from hidden terminals. We then present a novel protocol based on the DCF that uses an out-of-band busy tone as a negative acknowledgment to provide an efficient solution to the aforementioned problem. We extend our analytical model to the enhanced protocol and show that it preserves predictive accuracy. Most importantly, our numerical experiments confirm that the enhanced protocol improves the PDR by up to 10% and increases the supported vehicle density by up to two times for a range of packet arrival rates while maintaining the delay below the required threshold level.

The MAC access delay in a saturated IEEE 802.11 DCF wireless LAN is analyzed. We develop a unified analytical model and obtain explicit expressions for the first two moments as well as the generating function. We show via comparison with simulation that our model accurately predicts the mean, standard deviation, and distribution of the access delay for a wide range of operating conditions. In addition, we show that the obtained generating function is much more accurate than others that have appeared in the literature. Using our model, we prove that the binary exponential backoff mechanism induces a heavy-tailed delay distribution for the case of unlimited retransmissions. We show using numerical examples that the distribution has a truncated power-law tail when a retransmission limit exists. This finding suggests that DCF is prone to long delays and not suited to carrying delay-sensitive applications

A comprehensive energy model for wireless sensor networks is provided by considering seven key energy consumption sources some of which are ignored by currently available models. We demonstrate the importance of using such a comprehensive model by comparing it to other existing energy models in terms of the lifetime of a sensor node. We use our model to evaluate energy consumption and node lifetime for a sensor network with fixed configuration and we validate this evaluation by simulation. We show that existing energy models over-estimate life expectancy of a sensor node by 30-58% and also yield an "optimised" number of clusters which is too large. We further make the following two observations: 1) the optimal number of clusters increases with the increase of free space fading energy, 2) for sensor networks with 100 sensors over an area of 104-105 (m2), finding the optimal number of clusters becomes less important when free space fading energy is very low (less than 1670 pJ/bit/m2), while for larger networks, on the other hand, cluster optimization is still important even if free space fading energy is low. Guidelines for efficient and reliable sensor network design as well as extension to a sensor network with rotating cluster heads are provided.

Whether it be the passengers’ mobility demand in transportation systems, or the consumers’ energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement.