Cities with a growing population and complexity face urban crises related to road safety and mobility. Governments and collaborative stakeholders are introducing car-free day programs to address these challenges. In this study, a scientific criterion to identify suitable streets for such programs was established through a three-round Delphi study. The study involved twenty panelists from academia, city governments, international and local organizations, and freelance consultants. The panelists identified forty-six criteria clustered into six categories through open-ended and close-ended rounds, desk research, and insights from a car-free day workshop. The panelists reached a consensus on all categories and forty items. According to the results, the panelists reached a “good agreement” on three categories, environment, health & safety, land use & roadside, and mobility & accessibility; a “moderate agreement” on the three categories, namely, categories, geometry & functionality, and policy & governance; and a “weak” agreement on the one object, namely, social & economic. The relative importance index indicated that the two categories, “mobility and accessibility” and “environment, health and safety”, were considered the most important. The study suggests that these findings can potentially influence urban planning and transportation policies.

Identifying and prioritizing hazardous road traffic crash locations is an efficient way to mitigate road traffic crashes, treat point locations, and introduce regulations for area-wide changes. A sound method to identify blackspots (BS) and area-wide hotspots (HS) would help increase the precision of intervention, reduce future crash incidents, and introduce proper measures. In this study, we implemented the operational definitions criterion in the Hungarian design guideline for road planning, reducing the huge number of crashes that occurred over three years for the accuracy and simplicity of the analysis. K-means and hierarchical clustering algorithms were compared for the segmentation process. K-means performed better, and it is selected after comparing the two algorithms with three indexes: Silhouette, Davies–Bouldin, and Calinski–Harabasz. The Empirical Bayes (EB) method was employed for the final process of the BS identification. Three BS were identified in Budapest, based on a three-year crash data set from 2016 to 2018. The optimized hotspot analysis (Getis-Ord Gi*) using the Geographic Information System (GIS) technique was conducted. The spatial autocorrelation analysis separates the hotspots, cold spots, and insignificant areas with 95% and 90% confidence levels.

As crash data have distinctive behavior like over-dispersion, researchers have used statistical methods to deal with this unique behavior of crash data specifically. This study employed generalized linear modeling techniques to develop the model. It was assumed that the accident counts followed negative-binomial distribution, and the link function was chosen to be the log link function. Negative-binomial modeling technique was chosen over Poisson distribution because it is the most used technique by many researchers as crash data may encounter over-dispersion. The accident data set showed greater variability between its variance and mean. The accident frequency distribution is shown in this study that it is highly skewed, with a very high number of road segments registering zero accidents. Negative binomial distribution was chosen over Poisson distribution after comparing Akaike’s Information Criterion (AIC) and Bayesian Information Criteria (BIC). The method is widely applied to count data. Twenty-two parameters were estimated in the model. Since p < 0.05 in the omnibus test, the null hypothesis is rejected, which indicates that the model is reasonably fit. The strongest variables in the model were witnessed to be the length of the links, number of lanes, average daily traffic, bus lane, number of buses and trolleys, and HGVs.

A cost-benefit analysis in a road safety context fundamentally analyzes the advantage of higher safety or lower risk. It can help determine if increasing spending on road safety programs is cost-effective. This study estimates the value of statistical life (VSL) - the amount of money that might be justified to save one person's life. The VSL is calculated using the willingness to pay (WTP) data obtained through a contingent valuation survey. Three discrete choice models are developed: log-logistic, log-normal, and Weibull. The log-logistic model outperforms the log-normal and Weibull models, comparing Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. We consider the log-logistic model's mean and median WTP values to estimate the VSL value in the Ethiopian road transport safety context. The VSL estimate in the Ethiopian road transport safety context is 53.52 million ETB (USD 1.07 million). The respondents' median WTP is ETB 714.44 (USD 14.23). Although the study is in Ethiopia, the findings can be applied to other low- and middle-income countries (LMICs) for the same purpose with modifications. The research findings will aid in a better understanding of the economic efficiencies of increased spending on road safety initiatives. Future research could compare current trends in road safety investment to the amount that should be spent based on the economic justifications from this study.

Addis Ababa, the traffic-choked capital of Ethiopia with an ever-growing population, faces an urban crisis regarding road safety and mobility in recent years. To tackle these challenges, the government of Ethiopia and collaborative stakeholders launched a movement called Menged Le Sew (Streets for People), which takes place once a month since December 2018 in Addis Ababa and a few other cities in Ethiopia. The government's strategy to restrict cars once a month gives people an opportunity to reclaim public spaces. Residents of the cities use the open streets for different activities such as, participating in various sports, exercising, health campaigns, active mobility, and serving as a playground for children. To further promote the campaign nationwide and increase its longevity and aimed at serving as a guidebook for stakeholders organizing similar Menged Le Sew events, a toolkit was recently developed and launched by the Addis Ababa Traffic Management Agency and the World Resources Institute, Africa. However, the complexity of urban mobility, rising urbanization, and cross-sectoral interdependence necessitate a management strategy that blends new forms of governance and cooperation while emphasizing public participation to find and execute strategies to promote systemic change. Even though the movement has already started bearing fruit, to make the gains more sustainable and to race with the extremely growing mobility and urbanization challenges, this paper suggests a new framework and strategies of street experiments, taking the already existing and widely accepted movement (Menged Le Sew) as a potential ground. The study introduces the urbanization and mobility crisis in the country, then discusses the context of car-free days as a pathway for the sociotechnical transition towards sustainable mobility and proposes a framework for the actualization of this transition.

Recently, in the literature, microscopic simulation is one of the most attractive methods in impact assessment of automated vehicles (AVs) on traffic flow. AVs can be divided into different categories, each having different driving characteristics. Hence, calibrating microscopic simulators for different AV categories could be challenging in AVs' impact assessment. The PTV Vissim microscopic traffic simulation software has been calibrated for simulating diverse types of AVs in a large body of literature. There are two main streams of studies in literature adapting AVs' driving behaviors in Vissim following either internal (i.e., adjusting the parameters of the Vissim's default driving behavior models) or external (i.e., adapting AVs' behavior through external VISSIM interfaces) modeling approaches. The current paper investigates how the PTV Vissim has been internally calibrated for the simulation of different types of AVs and compares the calibrated values in the literature with default values introduced in the recent version of PTV Vissim. In the present paper, the reviewed studies are partitioned into two main categories according to the characteristics of the studied AVs, the studies focused on autonomous automated vehicles (AAVs) and the ones focused on cooperative automated vehicles (CAVs). Our findings indicate that the literature expects a lower value for parameters including standstill distance (CC0), headway time (CC1), following variation (CC2), the threshold for entering "following"(CC3), negative/positive following thresholds (CC4/CC5), speed dependency of oscillation (CC6), oscillation acceleration (CC7), safety distance reduction factor (SDRF), and minimum headway front/rear (MinHW) for AVs than conventional vehicles (CVs). Besides, the literature expects higher values for parameters including standstill acceleration (CC8), acceleration at 80 km/h (CC9), looking distances, and maximum deceleration for cooperative braking (MaxDCB) for AVs. When cautious AVs are introduced, deterring effects are expected in the literature (e.g., higher CC0). Moreover, CAVs can have higher looking distance values compared with AAVs.

The conducted literature review aimed to provide an overall perspective on the significant findings of past research works related to vehicle crashes and prediction models. The literature review also provided information concerning past road safety research methodology and viable statistical analysis and computing tools. Though the selection of a specific model hinges on the objective of the research and nature of the response, when compared to statistical modeling techniques, Artificial Neural Networks (ANNs), which can model complex nonlinear relationships among dependent and independent parameters, have been witnessed to be very powerful.

One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Given the same vehicle in a different timeframe, the drivers’ reactions to similar situations vary, which has a significant influence on the emissions and fuel consumption as their use of acceleration and speed differ. Because the driving patterns of automated vehicles are programmable and provide a platform for smooth driving situations, it is predicted that deploying them might potentially reduce fuel consumption, particularly in urban areas with given traffic situations. This study’s goal is to examine how different degrees of automated vehicles behave when it comes to emissions and how accelerations affect that behavior. Furthermore, the total aggregated emissions on the synthesized urban network are evaluated and compared to legacy vehicles. The emission measuring model is based on the Handbook Emission Factors for Road Transport (HBEFA)3 and is utilized with the Simulation of Urban Mobility (SUMO) microscopic simulation software. The results demonstrate that acceleration value is strongly correlated with individual vehicle emissions. Although the ability of automated vehicles (AVs) to swiftly achieve higher acceleration values has an adverse effect on emissions reduction, it was compensated by the rate of accelerations, which decreases as the automation level increases. According to the simulation results, automated vehicles can reduce carbon monoxide (CO) emissions by 38.56%, carbon dioxide (CO ₂) emissions by 17.09%, hydrocarbons (HC) emissions by 36.3%, particulate matter (PM _x) emissions by 28.12%, nitrogen oxides (NO _x) emissions by 19.78% in the most optimistic scenario (that is, when all vehicles are replaced by the upper bound automated vehicles) in the network level.

Keeping the basic principles of sustainable development, it must be highlighted that decisions about transport safety projects must be made following expert preparation, using reliable, professional methods. A prerequisite for the cost–benefit analysis of investments is to constantly monitor the efficiency of accident forecasting models and to update these continuously. This paper presents an accident forecasting model for urban areas, which handles both the properties of the public road infrastructure and spatial dependency relations. As the aim was to model the urban environment, we focused on the road public transportation modes (bus and trolley) and the vulnerable road users (bicyclist) using shared infrastructure elements. The road accident data from 2016 to 2018 on the whole road network of Budapest, Hungary, is analyzed, focusing on road links (i.e., road segments between junctions) by applying spatial econometric statistical models. As a result of this article, we have developed a model that can be used by decision-makers as well, which is suitable for estimating the expected value of accidents, and thus for the development of the optimal sequence of appropriate road safety interventions.

the purpose of road accidents forecasting is to analyze the tendency of road accidents under existing road traffic conditions, evaluate the feasibility and practical effectiveness of traffic safety measures reasonably, control the factors affecting road accidents, and reduce the traffic accidents. The characteristics such as non-linearity, randomness and uncertainty in traffic system make it difficult to forecast road accidents, the behavioral feature of traffic system. The objectives of this study are to determine the critical accident variables for accident prediction purposes, to utilize Artificial Neural Network (ANN) as a tool for accident prediction analysis, and to develop an Accident Prediction Model. This study uses a series of artificial neural networks to model and estimate accident by the applied software that is NeuroSolutions 6.0. An important implication of this paper after sensitivity analysis is that the variables like mixed-use developments, side-drain condition, guard rail, marker-post, chevron markings and curve warnings, cross-drainage works, over-head structures, Fly-over Bridge, side roads/access roads, under-pass, and volume of traffic are most significant factors which increase accident.