Speed-flow relationships have been established for different free-flow speeds on urban freeways. However, there have been few research efforts relating real-time traffic flow parameters and weather conditions for different levels of heavy vehicle traffic. This study aims at establishing relationships between speed and volume in freeway sections using Remote Traffic Microwave Sensor (RTMS) data as a function of weather conditions. Historical weather and RTMS detector data (i.e., volume and speed) from two highway corridors in the Istanbul metropolitan area are used for this purpose. Empirical relationships between traffic speed and volume are analyzed by weather condition (clear, rain, fog/mist/haze, or snow), surface condition (dry, wet, or icy), and percentage of heavy vehicles in the traffic mix. The findings from the analysis show that rain reduced the average vehicular speeds by 8 to 12% and the capacity by 7-8%. Moreover, wet surface conditions resulted in a reduction of average speeds by 6 to 7% and light snow affected demand leading to a significant reduction in traffic volume

Identifying the causes of road traffic crashes (RTCs) and contributing factors is of utmost importance for developing sustainable road network plans and urban transport management. Driver-related factors are the leading causes of RTCs, and speed is claimed to be a major contributor to crash occurrences. The results reported in the literature are mixed regarding speed-crash occurrence causality on rural and urban roads. Even though recent studies shed some light on factors and the direction of effects, knowledge is still insufficient to allow for specific quantifications. Thus, this paper aimed to contribute to the analysis of speed-crash occurrence causality by identifying the road features and traffic flow parameters leading to RTCs associated with driver errors along an access-controlled major highway (761.6 km of Highway 15 between Taif and Medina) in Saudi Arabia. Binomial logistic regression (BNLOGREG) was employed to predict the probability of RTCs associated with driver errors (p < 0.001), and its results were compared with other supervised machine learning (ML) models, such as random forest (RF) and k-nearest neighbor (kNN) to search for more accurate predictions. The highest classification accuracy (CA) yielded by RF and BNLOGREG was 0.787, compared to kNN’s 0.750. Moreover, RF resulted in the largest area under the ROC (a receiver operating characteristic) curve (AUC for RF = 0.712, BLOGREG = 0.608, and kNN = 0.643). As a result, increases in the number of lanes (NL) and daily average speed of traffic flow (ASF) decreased the probability of driver error-related crashes. Conversely, an increase in annual average daily traffic (AADT) and the availability of straight and horizontal curve sections increased the probability of driver-related RTCs. The findings support previous studies in similar study contexts that looked at speed dispersion in crash occurrence and severity but disagreed with others that looked at absolute speed at individual vehicle or road segment levels. Thus, the paper contributes to insufficient knowledge of the factors in crash occurrences associated with driver errors on major roads within the context of this case study. Finally, crash prevention and mitigation strategies were recommended regarding the factors involved in RTCs and should be implemented when and where they are needed.

This paper investigates the multicenter development course of Istanbul based upon urban travel patterns in the city. The spatial distribution of population and employment between 1970 and 2000 illustrated a decentralizing trend in the urban structure. Hierarchical clustering analysis was used to determine relatively homogenous groups of interactions and to explain the interaction patterns among the districts. The results of the study supported the multicenter development at the lower level on both continents (Asia and Europe), and at the upper level between the continents. Determination of efficient locations of new subcenters was suggested for further research.