Mapping of Traffic Conditions at Downtown Thessaloniki with the Help of GPS Technology P. D. Savvaidis and K. Lakakis Aristotle University of Thessaloniki, Department of Civil Engineering, Laboratory of Geodesy, GR Thessaloniki

A major problem in designing a traffic plan for an urban area: The availability of traffic data and the cost in time and money of their updating A major problem in designing a traffic plan for an urban area: The availability of traffic data and the cost in time and money of their updating The traffic conditions are characterized or can be simulated by: Mean vehicle speed Traffic volume, traffic density Traffic capacity etc. The traffic conditions are characterized or can be simulated by: Mean vehicle speed Traffic volume, traffic density Traffic capacity etc.

Observations carried out at certain points Movement of a vehicle along traffic routes Use of recording instrumentation Observations carried out at certain points Movement of a vehicle along traffic routes Use of recording instrumentation Traditional ways of obtaining traffic data:

Simple in practice Time consuming. Need a large number of observers to cover a road network. Provide data only for the period of the measurements. Updating the observed data in a future occasion requires the organization of a new survey. The results will again soon be out of date. Simple in practice Time consuming. Need a large number of observers to cover a road network. Provide data only for the period of the measurements. Updating the observed data in a future occasion requires the organization of a new survey. The results will again soon be out of date. These conventional techniques are:

Vehicle-based GPS receivers have been used in several occasions for the estimation of traffic conditions along an urban road network. The method usually employed is running along the roads in a vehicle equipped with a GPS receiver. Kinematic DGPS or Real Time Kinematic DGPS positioning can provide accurate enough results in most applications. To overcome these problems: A land navigation system was developed in the Laboratory of Geodesy, School of Civil Engineering.

VEhicle COntrol and Navigation System VECON System is basically a Fleet Management System, but it has been designed so that it can be used both for building its own digital map and for continuous monitoring traffic conditions in the sense of travel time and travel speed computations. VEhicle COntrol and Navigation System VECON System is basically a Fleet Management System, but it has been designed so that it can be used both for building its own digital map and for continuous monitoring traffic conditions in the sense of travel time and travel speed computations. Vecon is a Real Time DGPS system

An interesting question about the system is the accuracy of the measurements when using stand- alone GPS receivers now that SA (Selective Availability) was turned off. The use of stand-alone GPS can reduce the cost of the vehicle and the base station hardware. In this way it becomes possible for more vehicles to be used and more traffic data to be collected in a shorter period of time plus the continuous updating of the travel- time database of the road network. An interesting question about the system is the accuracy of the measurements when using stand- alone GPS receivers now that SA (Selective Availability) was turned off. The use of stand-alone GPS can reduce the cost of the vehicle and the base station hardware. In this way it becomes possible for more vehicles to be used and more traffic data to be collected in a shorter period of time plus the continuous updating of the travel- time database of the road network. SA (Selective Availability) off

In this paper the use of a simplified stand-alone GPS receiver for the collection of traffic data (travel times per road segment) is described and an evaluation of its performance is being done in field conditions.

The ability of the above mentioned simple system to record positional and time data was used for the determination of traffic data in a pilot project covering the downtown part of the city of Thessaloniki.

The vehicle traveled along major roads of the city center following the general traffic flow at two different time zones during the day, according to the expected traffic conditions (morning session – afternoon session), for a total of seven days (July, 3 to 11). The road network was divided into segments starting and ending at the intersections of the roads (or nodes of the network). The vehicle traveled along major roads of the city center following the general traffic flow at two different time zones during the day, according to the expected traffic conditions (morning session – afternoon session), for a total of seven days (July, 3 to 11). The road network was divided into segments starting and ending at the intersections of the roads (or nodes of the network). For each segment, the travel time between the starting and the ending node was computed from the GPS time data. Knowing the distance between the nodes and the respective travel time, the speed of the vehicle along the particular road segment was computed. The resulting measurements in NMEA format were transmitted to the VECON control center for storing and further processing. For each segment, the travel time between the starting and the ending node was computed from the GPS time data. Knowing the distance between the nodes and the respective travel time, the speed of the vehicle along the particular road segment was computed. The resulting measurements in NMEA format were transmitted to the VECON control center for storing and further processing.

A total of about 30 travels per road segment were accomplished in the time period of the project. A comparison of the positional data measured with the GPS to the digital map of the area gave very good results for most of the roads traveled. In a few cases no position could be computed due to the limited availability of satellites at narrow road segments between tall buildings or dense leafage of trees. Even in such cases there were positional data measured at the crossing of roads (where the visibility was better) so that the computation of travel times per segment was possible. A total of about 30 travels per road segment were accomplished in the time period of the project. A comparison of the positional data measured with the GPS to the digital map of the area gave very good results for most of the roads traveled. In a few cases no position could be computed due to the limited availability of satellites at narrow road segments between tall buildings or dense leafage of trees. Even in such cases there were positional data measured at the crossing of roads (where the visibility was better) so that the computation of travel times per segment was possible.

Following the field measurements, the measured data were driven into a computer program for the automatic computation of travel times for each road segment. The program is using an algorithm to find the measured GPS points nearest to the start and end node of each segment (their coordinates given). Traffic times computed include also delay times due to red traffic lights as this was considered a realistic approach of the true traffic conditions. Following the field measurements, the measured data were driven into a computer program for the automatic computation of travel times for each road segment. The program is using an algorithm to find the measured GPS points nearest to the start and end node of each segment (their coordinates given). Traffic times computed include also delay times due to red traffic lights as this was considered a realistic approach of the true traffic conditions. After the determination of the two (start and end) measured points the travel time for the road segment was computed as well as the mean vehicle velocity from the travel time and the known length of the segment.

Example 1: Measured travel times along Egnatia Str. (total of all segments) Example 1: Measured travel times along Egnatia Str. (total of all segments) Two examples of the computed travel times along the road network

Example 2: Measured travel times along Tsimiski Str. (total of all segments) Example 2: Measured travel times along Tsimiski Str. (total of all segments)

Computation of vehicle speed Knowing the distance between the nodes and the respective travel time, the speed of the vehicle along the particular road segment is computed.

Mean vehicle speed per road segment along Egnatia Str. for all days of measurement for morning observations.

Mean vehicle speed per road segment along Egnatia Str. for all days of measurement for afternoon observations.

Computed travel times per road segment were input to the ArcView Spatial Analyst program forming a preliminary time-cost database that can be used for the computation of shortest path between two points, an important task especially in cases of guidance of emergency vehicles in the urban road network.

Several random fluctuations of travel times can be attributed to accidental factors and unanticipated incidents, that however happen continuously in the city, making the real time update of the time database necessary. From the study of the available data it can be seen that there is an expected tendency of reduction of travel times at afternoon hours, whenever shops are closed, that is Monday and Wednesday.

Making a short study of the results of the GPS measurements, it is clear that a lot of useful conclusions for traffic conditions can be drawn. For example: It is justified that traffic becomes harder in the morning basically in the East to West direction, Egnatia Str. being a preferable way than Tsimiski Str. In the afternoons with low traffic volume it can be seen that traffic lights prohibit a continuous 50 Km/h vehicle speed all along the main roads as it was planned. For example: It is justified that traffic becomes harder in the morning basically in the East to West direction, Egnatia Str. being a preferable way than Tsimiski Str. In the afternoons with low traffic volume it can be seen that traffic lights prohibit a continuous 50 Km/h vehicle speed all along the main roads as it was planned.

The aim of the work was the utilization of GPS technology for the measurement of travel times along the city road network. The measurements were carried out with stand-alone GPS, without the application of differential corrections. Taking into consideration the positional accuracy of the measured trajectories, it was found that the resulting accuracy of GPS with SA off was better than expected. The accuracy given by the particular method, in relation always with the accuracy of the digital background that was used, was satisfactory, in some cases providing even the lane of the road where the vehicle was traveling. The accuracy was limited mainly in road segments with shading of satellites by tall buildings and trees. The aim of the work was the utilization of GPS technology for the measurement of travel times along the city road network. The measurements were carried out with stand-alone GPS, without the application of differential corrections. Taking into consideration the positional accuracy of the measured trajectories, it was found that the resulting accuracy of GPS with SA off was better than expected. The accuracy given by the particular method, in relation always with the accuracy of the digital background that was used, was satisfactory, in some cases providing even the lane of the road where the vehicle was traveling. The accuracy was limited mainly in road segments with shading of satellites by tall buildings and trees. Conclusions 1 Conclusions 1

Conclusions 2 Conclusions 2 This low-cost data collection method gave enough travel time measurements for a preliminary but detailed study of traffic conditions and provided numerical data for all the road segments in the area of the project. Having an approximate number of 30 vehicle passes per road segment some processing of the results gave the above-mentioned numerical data and figures. It is obvious that the way of data elaboration depends on the availability of travel times and their distribution over time. Then, the travel times used as costs in the GIS program may be chosen according to time of the day or as mean values over a pre-determined period of time or with the help of a time-model. Updating the time database is an easy task that can be done even in real time by employing existing vehicle fleets (e.g. taxi vehicles). This low-cost data collection method gave enough travel time measurements for a preliminary but detailed study of traffic conditions and provided numerical data for all the road segments in the area of the project. Having an approximate number of 30 vehicle passes per road segment some processing of the results gave the above-mentioned numerical data and figures. It is obvious that the way of data elaboration depends on the availability of travel times and their distribution over time. Then, the travel times used as costs in the GIS program may be chosen according to time of the day or as mean values over a pre-determined period of time or with the help of a time-model. Updating the time database is an easy task that can be done even in real time by employing existing vehicle fleets (e.g. taxi vehicles).