In Turkey, thousands of people die in road traffic accidents and hundreds of thousands are injured every year. This study aims to quantify the fatal traffic accident problem in Turkey. Quantifying the scale of the problem will direct the attention of decision-makers and related authorities in Turkey to tackle the traffic accidents’ problem. To accomplish these objectives, accidents’ data from 2009 through 2019 were used. The characteristics of fatal traffic accidents were investigated and models were developed. The accuracy of the model’s performance was evaluated using Root Mean Square Error and the Coefficient of Determination. The results showed that about (9%) of total accidents have resulted in human casualties. The analysis revealed that mistakes of drivers and pedestrians caused 88% and 8% of the total accidents respectively. Drivers' mistakes included violations of the speed limit, right of way and maneuvering rules. The developed models showed a strong correlation between the numbers of injuries and vehicles’ km/year (R2 = 0.84). Finally, the number of casualties in the last two years, showed a decrease as a result of a drop in the mobility rate of heavy vehicles in addition to a slowdown in the growth rate of registered vehicles.
Istanbul's public transport is very complex with many interactions between different modes of transportation, carriers and new projects. All of them had to be considered in this project, with the knowledge that the optimization had to be focused on the public bus transport provided by the IETT Company. Due to the lack of available basic demand data for private busses, only scheduled services can be considered and not passenger requested services.For public bus transport (mainly company busses), the demand data was gathered from counting data and the Intelligent Ticketing System (Akbil) data. This is the central basic data of the project. Additionally the data for bus, railway, light railway and seaway transportation were handed over and adapted to build up the digital public transport model.After the detailed analysis of the network model and its visualisation and reporting features, the weak points of the existing network were found (e.g. parts with low capacity usage of vehicles). Beside the very time intense development of the public transport model, the finding of adequate solutions and proposals for the amelioration of the actual situation was a main task of this project. Different general modes of optimization were found, described and defined in close adjustment with the IETT Company.The study shows the positive effects that can be achieved by different kinds of optimization. Further on, it gives practical examples for the different kinds of optimization and shows the results from the IETT Company -and passengerpoint of view. Some improvements are suggested for a more efficient public bus system. Finally, the numbers of the maximum possible savings for the complete region of Istanbul without cutting down the quality of transportation for passengers are given.With the realisation of the suggested improvements for the IETT Company bus traffic, the IETT Company can save a lot of money. The passengers will find faster bus lines and reduced travel times; however, the number of transfers between lines will increase. With the supposed acceleration of bus transportation on the future backbone network, there will be a flexible, high quality and reliable bus transportation system for Istanbul to face the challenges of the future.
This study investigates the effects of traffic management strategies on performance of a work zone along arterial roads. The strategies included Temporary Access Control (TAC), Limitation of Heavy Vehicles (LHV) and Lanes Management (LM). Study area was conducted on Queen Rania Al-Abdullah Arterial Street (QRAA) in Amman, the Capital of Jordan. Traffic volume was collected through field observation during morning rush hours. Then, a micro simulation model was developed based on empirical traffic data using VISSIM software. The simulation model has been validated using different parameters. The analysis showed that the average delay was highly affected by a lot of factors like percent of heavy vehicles, flow from access roads, parking, driver behavior and lanes monitoring. Moreover, the average delay was 113.5 second/vehicle and average speed was 17 km/hr. furthermore, speed has decreased from 22 km/hour before work zone to 10.1 km/hour at work zone (-54 %) . On the other hand, by applying the traffic management strategies, both of delay and speed were improved. For instance, average delay was decreased from 113.5 to 89 second/vehicle (-22%) and average speed showed improvement by 30 % (from 22 to 29) km/hr.
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