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The deceleration lane isbefore numbers to minus signs. a critical part of the freeway, enabling vehicles to exit the expressway safely and in an orderly fashion. However, drivers are human and thus make subjective decisions while driving; as such, each driver may approach and traverse the deceleration lane differently. This variance, which can cause major traffic disruptions and collisions, can be observed -and even mitigated, as proposed herein -based on a driver's particular characteristics. To study the variances in the longitudinal vehicle positions and microscopic operating characteristics of different drivers on a freeway exit area, a field operational test involving 46 subjects was carried out to collect data on driver characteristics, vehicle motion postures, micro-driving operations, and road geometric elements. The 46 participants were observed based on experience and gender, and the mathematical statistical method was used to analyse driving differences in the deceleration lane. The results show that (1) the vehicle motion state can be divided into four operational stages on the deceleration lane: the pre-deceleration process, the dynamic adjustment process, the first braking process, and the second braking process; (2) drivers generally adopted deceleration behaviour rather than maintain uniform speed when driving in the taper; (3) about 50% of drivers braked after entering the deceleration lane; (4) male drivers and skilled drivers were more inclined to drive at a higher speed on the deceleration lane, and female drivers showed a sharp increase in braking frequency once they travelled 110-150 m downstream of the taper starting point. The results of this study provide data and insights for deceleration lane design, traffic management, and driver training.How to cite this article: Lyu, N., et al.: Exploring longitudinal driving behaviour on a freeway deceleration lane using field operational test data. IET Intell. Transp.

Real-time regional risk prediction can play a crucial role in preventing traffic accidents. Thus, this study established a lane-level real-time regional risk prediction model. Based on observed data, the least squares-support vector machines (LS-SVM) algorithm was used to identify each lane region of the mainline, and the initial traffic parameters and surrogate safety measures (SSMs) were extracted and aggregated. The negative samples that characterized normal traffic and the positive samples that characterized regional risk were identified. Mutual information (MI) was used to determine the information gain of various feature variables in the samples, and the key feature variables affecting the regional conditions were tested and screened by means of binary logit regression analysis. Upon screening the variables and corresponding labels, the construction and verification of a lane-level regional risk prediction model was completed using the catastrophe theory. The results showed that lane difference is an important parameter to reduce the uncertainty of regional risk, and its odds ratio (OR) was 16.30 at the 95% confidence level. The 10%-quantile modified time to collision (MTTC) inverse, the speed difference between lanes, and 10%-quantile headway (DHW) had an obvious influence on regional status. The model achieved an overall accuracy of 86.50%, predicting 84.78% of regional risks with a false positive rate of 13.37% and 86.63% of normal traffic with a false positive rate of 15.22%. The proposed model can provide a basis for formulating individualized active traffic control strategies for different lanes.

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In complex traffic environments, collision warning systems that rely only on in-vehicle sensors are limited in accuracy and range. Vehicle-to-infrastructure (V2I) communication systems, however, offer more robust information exchange, and thus, warnings. In this study, V2I was used to analyze side-collision warning models at non-signalized intersections: A novel time-delay side-collision warning model was developed according to the motion compensation principle. This novel time-delay model was compared with and verified against a traditional side-collision warning model. Using a V2I-oriented simulated driving platform, three vehicle-vehicle collision scenarios were designed at non-signalized intersections. Twenty participants were recruited to conduct simulated driving experiments to test and verify the performance of each collision warning model. The results showed that compared with no warning system, both side-collision warning models reduced the proportion of vehicle collisions. In terms of efficacy, the traditional model generated an effective warning in 84.2% of cases, while the novel time-delay model generated an effective warning in 90.2%. In terms of response time and conflict time difference, the traditional model gave a longer response time of 0.91 s (that of the time-delay model is 0.78 s), but the time-delay model reduced the driving risk with a larger conflict time difference. Based on an analysis of driver gaze change post-warning, the statistical results showed that the proportion of effective gaze changes reached 84.3%. Based on subjective evaluations, drivers reported a higher degree of acceptance of the time-delay model. Therefore, the time-delay side-collision warning model for non-signalized intersections proposed herein can improve the applicability and efficacy of warning systems in such complex traffic environments and provide reference for safety applications in V2I systems.