Using GPS for Determining Level of Service for major and minor collectors in Lattakia City Center

Lattakia City has faced many negative impacts of the extending crisis that Syria lives, such as defensive arrangements of most governmental facilities, and refuging millions of other Syrian cities citizens. This new situation was reflected on the traffic conditions on most streets and roads of city, especially on center streets. In addition it caused many traffic congestions, which led to investigate and analyze the current situation and finding traffic solutions for reducing these congestions on center streets. In this paper, the “moving observer” concept was used to measure travel time and average speed on major and minor collectors in Lattakia center. The vehicle was equipped with a GPS to collect necessary information, a number of trips was executed during daily peak period and free flow speed; then the data was processed and analyzed to generate max, min, and average speeds, constructing LOS for these streets. Study disclosed low values of LOS for some major collectors in the city, and expressed the need to take urgent arrangements to increase LOS on streets. The efficiency of “moving vehicle” concept was proved in implementing traffic studies.

Building hydrological model for designing culverts using GIS Technique

Hydrological studies in Syria- in general- based on classical methods to derive catchment areas of road culverts or estimating the design storm for these catchments. Such operations have a significant degree of complexity, effort and time costing, up to implementing clear concept about streams crossing the road and deriving the catchments boundaries to estimate the designing storm discharge according to road classification and passing facility type preventing road inundation or demolition. In this study the GIS technique was used to derive catchments for all outlets on this road and the morphological properties were estimated. The developed hydrological model for studied road allows designers to evaluate more accurate solutions in such engineering cases like determiningthe best locations for culverts on roads, estimating design storm and design discharge for those outlets and all other engineering characteristics “designing parameters” such as longitudinal slope, span, area, number of openings. as it allows the development of various economic solutions in order to choose the best one from both engineering and economic view points, according to road classification and storm probability.

Traffic Congestion Assessment Based on Speed Index Performance on Arterial Streets in the Commercial Center of Latakia

The high traffic density causes traffic congestion on the road, causing delays before it reaches its destination, in addition to the stress and high psychological impact on the driver, it also results in staff and staff being late for work and students being late for school, With frequent accidents on these busy roads. Congestion has a lot of reasons, one of them is poor infrastructure that leads to a state of congestion, a significant increase in the number of vehicles, a lack of prior information for road users about the traffic situation, Division of traffic management into several departments (Public works, municipality, security, transport…). They become ineffective in some areas, aggressive driving style Which may be an effective factor in creating congestion, in addition to the use of individual transport rather than mass transport. Latakia suffers from a very poor transport system, and the indiscriminate movement of vehicles on its streets. This has been accompanied by increased traffic, which has led to many problems. The most important is the problem of congestion, in terms of increasing the values of delay times in different road sectors. This has contributed to major traffic crises and congestion. It affected service levels on arterial streets in the city's downtown, in the absence of rush-hour studies. Two main streets have been selected in the commercial and downtown center of Latakia City, which is an important arterial street for the distribution of movement. These arterial streets connect the commercial and administrative center of the city with the other parties of the city. The moving controller analyses a large number of actual speeds of the vehicle, measured in each sector, with a view to estimating congestion, calculating its indicators, and determining peak hours. Congestion levels were inferred based on the values of the speed performance index, and the average speeds of daytime and evening peak hours have been mapped to considered street sectors.

Predicting of Traffic Accident in Lattakia City Using Artificial Neural Networks

This study constitutes a preliminary step to develop a mathematical model for predicting traffic accidents in the city of Lattakia, based on a number of external factors, which include engineering characteristics, traffic incursions, and traffic accident data. As for its main goal, it is to reduce the number of traffic accidents expected in the future on the main streets in the city, as the study was conducted on various arterial streets in them in terms of their importance and in terms of the number of traffic accidents recorded on them, and in terms of the diversity of their engineering characteristics, in order to have sufficient familiarity with the traffic conditions in The city for various reasons, does not depend on the human behavior of the drivers or on the characteristics of the vehicle. A statistical analysis of traffic accident data for the years 2014, 2015, 2016 and 2017 was conducted on urban streets in Lattakia, where accidents were classified according to their severity, time of occurrence and place of their occurrence, and the necessary data were collected and digitized within a software environment in Microsoft Excel, and then a model was built Predicting the use of the artificial neural networks tool in the MATLAB program, in which data for 319 traffic accidents that were recorded in the years 2015, 2016 and 2017, were entered, which were divided into three groups (training, validation and testing). The structural neural network (10-10-1) gave high values ​​of the correlation coefficient, as the total R value during the three stages was 0.931236, which is very close to one, and therefore the designed network is ideal and achieves the response to predict traffic accidents monthly with very high accuracy.