Shlomo Bekhor Transportation Research Institute Technion – Israel Institute of Technology Monitoring and analysis of travel speeds on the national road network using floating car technologies

 Speed is considered a leading cause and contributing factor that affect injuries from road crashes.  There are numerous studies linking travel speeds and road crashes.  Hence an essential part of every road safety plan is speed management.  In order to manage speed, it has to be systematically and consistently monitored and analyzed.

 In this study we present a system for the collection and analysis of travel speeds at the nationwide level.  The current research provides a comprehensive speed database in space and time.  This analysis can identify the road sections with significant excesses of travel speeds relative to the speed limit.  It can also serve as baseline to evaluate current counter-measures employed to reduce speed.  The project was sponsored by Or Yarok (NGO).

 The conventional methods to measure speed are based on the deployment of equipment in the measured road sections, whether temporary or permanent (OECD, 2006).  Due to relatively high cost of the equipment and the deployment/installation, speed measurements are typically conducted at a low frequency in road locations that are selected for different considerations, and constitute only a very small part of the road network.  These methods allow performing targeted tests, but they do not supply systematic data bases for evaluation of actual speeds distributions in time and space.

 Vast penetration of GPS devices and cellular phones.  Speed assessment of equipped vehicles by cellular phones and by GPS to characterize speeds in the road network.  Focus was on average speed in congested conditions, often for navigation purposes.  Similar methods can be used to receive estimates of speed distribution during free flow conditions,  which are needed to monitor, analyze and manage road safety.  The advantage of these methods stems from the availability of the data,  without a need to install or deploy equipment of any sort.  The travel speeds used in this study were provided by Decell Technologies (a private company).

 To assess the reliability of the GPS speed data, it is needed to compare it to an independent data source.  The Ayalon Highway, a North-South highway crossing the Tel Aviv Metropolitan Area, was selected for the comparison.  Magnetic loop detectors permanently installed in the highway provide speed and occupancy data every five minutes, for each lane and direction.

 Ayalon speed data is obtained from averages taken in five minutes intervals.  The data are classified into five categories of different vehicles.  The average speed is calculated for each vehicle category.

Directio n From JunctionTo JunctionLoop Detector speedsGPS speeds Relative diff. Average (km/h)Std. dev. (km/h)Average (km/h)Std. dev. (km/h) North Kibutz GaluyotLa Guardia % La GuardiaHashalom % HashalomHarakevet % HarakevetHalacha % HalachaRokach % RokachKakal % KakalGlilot % GlilotShevat Hakochavim % South Shevat HakochavimGlilot % GlilotKakal % KakalRokach % RokachHalacha % HalachaHarakevet % HarakevetHashalom % HashalomLa Guardia % La GuardiaKibutz Galuyot % Average %

 Results from the comparative analysis of GPS data based on floating sources with sensor data on fixed locations show that there is a good fit.  Since the average segment length is about 6.0 km, and a GPS reading is recorded every 30 seconds on average, this means that a vehicle traveling at 90 km/h will give on average 4 GPS readings per segment.  At free-flow conditions, there are not many cars passing, so the only way to collect sufficient information is to gather them for a large period of time (in this study, 6 months).  In order to reduce variance and receive representative estimates it is recommended to gather at least 300 observations for every road section.

 The road network used for the analysis includes the 2011 Israel TMC road network.  This network contains most interurban roads and major arterial streets in metropolitan areas (Haifa, Tel Aviv and Jerusalem).  The network comprises 1,593 road segments with an overall length of about 8,960 Km.  Out of the 1,593 segments, 1,383 relate to interurban roads and 210 to urban arterials.

 The raw data was collected for 6 months, from 01‐Feb‐2011 until 28‐Jul‐2011, and after filtering included over 30 million GPS free-flow speed observations.  More than 90% of the 1,593 road segments have more than 1,000 observations.  For privacy reasons, there is no information about the driver or the vehicle, so the same segment might contain more than one observation for the same vehicle.  The data file contains the distribution of the speed for each road section at 5 km/h intervals.

 mean speed  standard deviation  percentage observations over the allowed speed  the 85th percentile  the excess speed (the difference between the 85th percentile speed and allowed speed).  A total of 6 different periods were defined:  3 typical days: Workdays (Sunday to Thursday), Friday and Saturday  2 time periods: day (from 06:00 to 22:00) and night (from 22:00 to 06:00).

Period Speed limit (km/h) Mean speed (km/h) Standard deviation (km/h) Percentage above speed limit 85 th Percentile speed (km/h) Excess speed (km/h) Workday – Day % % % % % Avg % Workday - Night % % % % % Avg %

Period Speed limit (km/h) Mean speed (km/h) Standard deviation (km/h) Percentage above speed limit 85 th Percentile speed (km/h) Excess speed (km/h) Workday – Day % % % % % Avg % Workday - Night % % % % % Avg %

 The average results are in line with previous studies, in the sense that about 50% of the drivers are speeding.  The average speed at night is significantly higher compared to free-flow day average speeds.  In addition, the average speed on weekends is significantly higher compared to weekdays.  The tables also shows that for roads with allowed speed of 110 km/h, the excess speed is lower compared to 100 km/h roads.  This might be explained by the fact that both cases are related to freeways, with relative similar geometric characteristics, and therefore the average differences between the two cases is relatively small (6-7 km/h) compared to the difference in the posted speed (10 km/h).

Vehicle Type Region Mean speed (km/h) Standard deviation (km/h) Percentage above speed limit 85th Percentile speed (km/h) Average Excess speed (km/h) Private carNorth % TruckNorth % BusNorth % Private carCenter % TruckCenter % BusCenter % Private carSouth % TruckSouth % BusSouth %

 The average speeds in the Center Region are higher than other regions  because of the higher share of multi-lane highways (with correspondent higher speed limits).  The excess speeds of private cars are higher in comparison to trucks and buses.  However, in the North and Center regions, it is noticeable the high excess speeds for buses.  Excess speeds for trucks are relatively low, which might be explained by speed limiters installed in trucks.

 This paper illustrates the application of a GIS tool to analyze GPS free-flow speeds at a national level.  The paper presents selected results, which can be easily derived from the system.  These results can serve as a decision support tool for speed management.  In particular, stake-holders and road safety organizations can utilize this system to monitor, evaluate, focus and maintain measures related to speed management.

 The research is still on-going, and GPS data is continuously being collected, thus allowing the monitoring of trends in observed speeds.  In particular, the research can support a recent project that installed cameras in several interurban road sections, by performing before and after studies not only on the specific road sections, but also on nearby sections.  The next phase of the research will combine information of road crash data with the speed data, and allow analysis of correlations between actual speeds and crashes.