Chapter 4 Methodology
4.6 Data analysis
4.6.2 Statistical analysis
To analyse the all type of data, in the first step, descriptive statistics were used to describe or summarise the data. Descriptive statistics was used as an exploratory method to examine the variables of interest before inferential statistical analysis was conducted. Research questions that were proposed in this study were addressed using proper and justified statistical tests.
In a survey study, the same participants are being measured on a different variable using the same measurement scale. Multiple regression is used to determine a participant attitude on speeding, speed enforcement and safety based on multiple independent variables.
For experimental data that produce participants’ choice of speed (responses) during the experiment, speed profile graphs were plotted to graphically show participants’ choice of speed while experiencing different types of treatment along the test routes. Among eight
groups in this experiment, three groups received single intervention and one group
experienced no intervention. Their data were used to analyse the single intervention effect.
The combined interventions’ analysis was conducted after that.
The drivers’ choice of speed was analysed using appropriate statistical tests to determine if there are differences in the mean scores of a dependent variable between three or more conditions/treatments, either as single or combined intervention. No pre-test measurement was taken by the participant. Statistical tests were utilized, such as one sample test, paired t-test, One-way ANOVA, and Factorial ANOVA. If there are any differences, a post hoc test or custom contrasts is applied to know where any differences lie.
In addition, the Kaplan-Meier method or test for survival analysis was used to understand the Halo effect (based on distance until driving above the speed limit) for participants who experience the presence of police, included when it was combined with other interventions.
Prior to running a statistical test, basic requirements and assumptions were checked and tested. The justification for each statistical test employed is explained in detail in the statistical analysis sections.
Chapter 5
Preliminary study for further development of methodology
5.1 Introduction
The purpose of this study is to develop more effective speed enforcement measures in order to influence motorists’ choice of speed. In order to maximise the impact of any intervention, factors such as road characteristics, traffic characteristics, and accident records should be considered. Consequently, road, traffic, and accident data were considered before selecting an appropriate study site and participants.
However, obtaining data and information regarding road traffic, primarily speed related data, was difficult in Indonesia. As a result, to fulfil this requirement, it was necessary to conduct a preliminary study phase that focused on the use of a traffic survey to collect and analyse traffic speed data on different road sections in Indonesia.
The survey was initially intended to identify the free-flowing speed of traffic to ensure that motorists are not prevented from driving at the speed of their own choice by the proximity of other vehicles. In free flow conditions, headways and lateral displacements are usually large;
for instance, a 3— 5-second separation may be present between cars (Highways Agency, 2005). Free-flow traffic is one of the circumstances which would provide insight that would assist in this investigation of motorists’ choice of speed.
This chapter describes the study site selection process for the experimental phase of this study (see Chapter IV), which consisted of accident data collection for the proposed location, as well as road condition and traffic survey results that have been conducted on the proposed
location. Also, as part of this study, constructive discussions were carried out with the Indonesian National Police Traffic Corps (INPTC) and related stakeholders concerning the speed enforcement experiment.
Therefore, from of March 9th–21st, 2015, road condition surveys, mid-link classified counts, and spot speed surveys were performed at six locations in Indonesia. Surveys were run at a
morning peak, between 07.00hrs and 09.00 hrs, and during off-peak hours, between 10.00 hrs and 12.00 hrs. Simultaneously, road inventory portrayed the road geometry and layout,
including the pavement conditions, the number of lanes, and the occurrence of any provisions for road users (i.e. pedestrian facility, bus shelter).
5.2 Road and traffic survey method
Initially, the purpose of this survey was to provide information that could be used for the selection of sites, vehicle types, ideal participants’ characteristics, and optimal procedures of the experiment. Therefore, it was necessary to conduct a road traffic survey in each of the six proposed sites, consisting of a traffic count and a spot speed survey. In addition, this initial survey was also planned to investigate road condition, particularly surface quality, layout and geometry, in order to select an appropriate survey location, refuge area, roadside publicity locations and checkpoint for the researcher and police team, including the speed gun operator.
5.2.1 Defining criteria for the sites
The choice of speed is influenced by a number of factors, including driver-related factors, vehicle factors, road and environment-related factors, and traffic-related factors (Haglund, 2001; Elliott and Broughton, 2005; Allan Quimby, Maycock, Palmer and Grayson, 1999; Allan Quimby, Maycock, Palmer and Buttress, 1999; Maycock, 1997; OECD/ECMT, 2006). The first step of the site selection process was to develop criteria for how to select study locations.
Therefore, based on the literature cited above, the criteria specified, among others, are road characteristics (urban/rural, single/dual carriageway), traffic flow, traffic speed, speed limit, vehicle type, records of speeding violations, and records of fatal accidents. Besides, for the present study, one of the most critical requirements was the proximity to training facilities to be used during the experiment.
INPTC agreed to provide access to two of their reputable training centres in Indonesia. The first one, a traffic police training facility, is in Pusdik Lantas Polri in Tangerang city (west of Jakarta), and the second is Riau Safety Driving Centre in Pekanbaru city. A room and computer to be used during the data collection were provided. Therefore, the study sites needed to be close enough to the training facilities to allow for effective, efficient, and convenient training for participants.
5.2.2 Accident data extraction for proposed location
Based on these criteria, the next step was to explore the accident records using the police accident database. The Indonesian police accident database is managed in an electronic form by the Law Enforcement Division of INPTC, which is called Integrated Road Safety Management (IRSMS) database. IRSMS provides comprehensive traffic accidents data, particularly with the
availability of crash locations in the form of GPS coordinates which can be displayed on Google Map.
The selection process was followed by an investigation of speed related accidents. Each accident record needs to be assessed which also be discussed with police investigator to assure the speed related accidents were happen the selected accidents. Only accident records with injury casualties were selected, which means damage-only accidents were excluded.
5.2.3 Road condition survey
The road condition survey was a survey to collect data on road characteristics, particularly road dimensions and geometry. The road survey consists of measurements of the length of road, the width of the road, the number of traffic lanes, shoulder width, median width, width of the sidewalk, horizontal alignment, and vertical alignment.
In the present study, road condition surveys were carried out using a car equipped with a camera. Trips using this car were completed in both directions, and the results then discussed with a police team to decide the appropriate location for conducting the traffic survey (i.e., traffic count and spot speed survey) and media placement for roadside publicity. A refuge area to be used in the experimental study also needed to be identified.
The survey was conducted manually by taking notes and photos. Some relevant information regarding road characteristics and conditions were discussed with local police and the road authority, such as black spots related to speeding, road status, road layout and geometry, road surface quality, roadside activity, speed limit, and traffic stream.
5.2.4 Traffic count
Traffic counts were taken to record the number of vehicles using a traffic lane. The information collected in the present study includes direction of traffic, classification of the vehicle, peak and off-peak hour volume, and average daily traffic. Therefore, based on the vehicle flow surveys guideline (O’Flaherty, 2007; Highways Agency, 2011) several specifications have been determined, such as:
1. The survey was carried out over two weekdays and one weekend,
2. The survey was started at 07.00 and finished at 09.00 for morning period and 10.00 to 12.00 for afternoon period, and
3. Vehicle classifications were distinguished in five types: motorcycle, car, bus, LGV and HGV.
In general, counts were conducted manually, using locally recruited staff under the supervision of the researcher. The observer used a simple mechanical hand counter with five tally counters. Each tally counter was labelled with types of vehicles. Two surveyors were
assigned to observe and record all the traffic that passed by within a 10-minute period.
Observers were stationed at a refuge area, which was a space with a suitable lay-by width of at least 10 metres, covered by traffic cones. When the counting period ended, the data was recorded in a prepared survey form.
5.2.5 Spot speed survey
Various methods are well known to study traffic speed, such as measuring the travel time over a certain distance manually using a stopwatch or mechanically using devices such as
pneumatic pipes. Another method is to record the vehicle speed electronically using devices such as speed radar gun or by using ultrasonic or infrared (O’Flaherty, 2007; Highways Agency, 2011; Skszek, 2004; De Waard and Rooijers, 1994) (see Figure 31).
Figure 31: Speed gun display used in spot speed survey
In this study, vehicle spot-speeds were obtained by using a police laser speed gun, positioned alongside the road. Several specifications were determined to allow for better speed survey results, such as:
1. The survey was unobtrusive to all road users; the operator and the equipment were not visible to motorists.
2. The speed gun was mounted inside an unmarked vehicle that was parked off the main carriageway in a prepared refuge area and set to record traffic from the facing direction only along a 400-metre range.
3. The length of observation road section was approximately 400 metres, starting from a specific point (such as a telephone/electricity pole or a building) and finishing at the point where the observer stood.
4. Since it was not possible to record the speed of all vehicles, surveys picked up the speed of every fifth vehicle as a sample regardless of vehicle type.
5. Vehicle types were distinguished into five types: motorcycle, car, bus, LGV, and HGV.
6. The speed was recorded in kilometres per hour (km/h).
Two surveyors were assigned to observe and record as much information as possible about vehicle speed within a ten-minute period. When the target vehicle’s speed was acquired, an observer who operated the speed gun read aloud the spotted speed and vehicle type. The other observer then logged these data in the prepared survey form. Similar to with traffic count, the period of observation was 2 hours, divided into 10-minute cycles.
5.3 Accident data investigation result
The accident records around Jakarta and Pekanbaru were collected from the police accident database for a period of two years (2013 and 2014). Records of 13,211 accidents were found around Jakarta and 641 accidents around Pekanbaru. The selection process continued through an investigation of speed-related accidents, which was a difficult part of the process due to accident records being inaccurate and incomplete. Instead of continuing to investigate the accident reports, I ask the officers help to indicate the location of accident with speed as the contributory factor, based on officers’ experience and accident record in my hand. Police investigators indicated some locations as accident spots that were potentially have speed a related factor. Six locations were nominated to be investigated further: two sites in Pekanbaru and four in Jakarta, as shown in Figure 32.
Figure 32: Map of proposed survey locations in proximity to training facilities
The selection area was, then, focused down to particular roads to allow for the assessment of each accident record. Only accident records with injury casualties were selected, which means damage-only accidents were excluded. The results of this selection process are presented in Figure 33. A red pin in the map represents an accident where at least one death occurred; an orange pin represents an accident where there was at least one seriously injured casualty; and a yellow pin represents an accident where there was at least one slight injury.
Figure 33: Example of map with plotting of accident data around a survey location.
Then, accident records nearby to proposed survey location were summarised in Table 7 below.
Table 7: Accident record of survey locations based on IRSMS data 2013-2014
Site no. Location Number of accident Number or casualties Fatal Serious Slight
1 Bekasi 57 6 64 11
2 Cikupa 23 2 20 24
3 Cibungur 47 13 18 44
4 Kalijati 50 15 46 33
5 Kulim 28 9 9 27
6 Rimbo 25 13 18 26
The highest accident number was found in Bekasi, where 70 fatal or seriously injured casualty was recorded.
5.4 Road and traffic characteristics of survey result
All selected sites were along major roads connecting rural and urban areas. In addition, the chosen spots also provided connection links between two cities. Road type, area development, speed limit, and road surface information were observed. A summary of road survey results is presented in Table 8.
Table 8: Survey locations and road characteristics
Site no. Route Road type Lanes Area Speed Limit Road surface
1 Bekasi Dual Carriageway 2 Urban 50 Good
2 Cikupa Dual Carriageway 2 Urban 50 Good
3 Cibungur Single Carriageway 1 Rural 60 Good
4 Kalijati Single Carriageway 1 Rural 60 Good
5 Kulim Single Carriageway 1 Urban 50 Good
6 Rimbo Dual Carriageway 2 Rural 60 Good
Among the six selected sites, there were four single carriageways and two dual carriageways.
Three of the selected sites were urban with a posted speed limit of 50 km/h, while the other three were rural sites with a posted 60 km/h speed limit.
The proposed sites 1, 2, and 6 were dual carriageways, having two lanes for travelling in each direction. The other three sites were single carriageways, featuring a single lane in each direction. The surveys also confirmed that the road surface in all locations was in good quality, good condition, which made it possible to obtain accurate results from the speed survey. An uneven road surface and pothole would make the free-flow travel difficult.
5.5 Traffic counts results and analysis
The traffic volume survey for the morning period started at 07.00 hrs and finished at 09.00 hrs, while the afternoon period took place between 10.00 hrs and 12.00 hrs. The survey was conducted on weekdays and weekends, except at the Kalijati location, where the weekend survey was interrupted due to severe weather conditions. The volume of traffic recorded in the survey form was the volume of vehicles approaching the observer in all lane only.
The traffic count survey successfully recorded 92,278 vehicles across the six survey locations.
The highest volume of vehicles was recorded in Cikupa during weekday mornings, while the lowest was in Kulim on weekend mornings. Motorcycles accounted for 67.94% of the observed traffic. The average volume per hour for each location is presented in Figure 34.
Figure 34: Average number of vehicles and converted passenger car unit (PCU)10 per hour for at all survey locations
10Passenger car unit (PCU) is a metric used in transportation engineering to assess traffic flow rate on a road. This study used the Indonesian Road Study Manual’s values for PCU as
994
Weekday Weekend Weekday Weekend Weekday Weekend Weekday Weekend Weekday Weekend Weekday Weekend
Bekasi Cikupa Cibungur Kalijati Kulim Rimbo
Number of vehicles
MC Car
Bus LGV
HGV PCU
An interesting result was found at the Cibungur and Rimbo sites, where the weekend traffic volume is higher compared to weekday traffic. A possible explanation for this trend is that trips to Cibungur and Rimbo are associated with leisure trips to popular tourism or shopping places which attract residents to spend a weekend at or around these two sites.
Three locations, Bekasi, Cikupa and Cibungur, have a traffic volume of around 1000 PCU (refer to footnote 10 above) per hour both during weekdays and weekends, which means it is difficult to find free flow of traffic in these locations. Kulim and Rimbo, on the contrary, have a lower flow for both weekdays and weekends. The difference between morning and afternoon traffic was found to be minor, except in Cikupa.
The highest proportion of motorcycles to other vehicles out of all six locations was found in Bekasi. In contrast, the highest proportion of cars was recorded in Kulim, and the lowest in Bekasi. The average proportion of cars to other vehicles for all locations is 24%. To sum up, motorcycles and cars are the most likely targets of traffic enforcement to be included in this study, since they were the most common type of vehicle observed in all locations.
In conclusion, high volumes were observed in Bekasi, Cikupa and Cibungur, on both weekdays and weekends; also, free-flowing traffic would not be likely to be found on a weekday in Kalijati. The Kulim and Rimbo locations would provide a greater chance of observing free-flowing movement. Nevertheless, the spot speed survey data is needed to confirm the results of this traffic count survey before it can be concluded which location is best to be the site of the study.
5.6 Spot speed survey results and analysis
5.6.1 Overview of spot speed data
A total of 22,372 observations were made at the six selected sites. The minimum speed recorded was 1 km/h, and the maximum was 104 km/h. The highest mean speed was 57.33 km/h (SD = 11.53), observed at the Rimbo site, while the lowest mean speed was 40.31 km/h (SD = 10.32) at the Cikupa site. Table 9 shows the results of the spot speed survey.
Most of the mean speeds were below the speed limit, except for at the Kulim site, where the mean speed was found to be 51.61 km/h, which is 1.61 km/h higher than the speed limit. In contrast, all 85th percentile speeds were equal to or higher than the speed limit. The highest proportion of vehicles travelling above the speed limit was recorded in Kulim: a proportion as
follows: car = 1 PCU, motorcycle = 0.3 PCU, LGV = 2.5 PCU, Bus = 3 PCU, and HGV = 3.5 PCU.
high as 51.6%. Even, 20.5% of motorists in Kulim travel at a speed 10 km/h higher than the posted speed limit.
Table 9: Summary of spot speed survey for all locations
Statistics Survey location
Bekasi Cikupa Cibungur Kalijati Kulim Rimbo
No. observation 2105 3656 4983 2817 4540 4271
Speed limit km/h 50 50 60 60 50 60
Mean 42.36 40.31 52.42 46.40 51.61 57.32
Median 41.00 40.00 52.00 46.00 51.00 57.00
Std. Deviation 10.42 10.32 10.18 9.37 10.76 11.53
Maximum 81.00 80.00 96.00 82.00 97.00 104.00
85th percentile 53.00 50.00 62.00 57.00 63.00 70.00
% Exceed 20.1 14.5 21.1 7.9 51.6 37.5
% Exceed +5 km/h 11.4 6.9 10.9 1.6 34.8 23.8
% Exceed +10 km/h 6.0 2.4 4.7 .6 20.5 12.9
The cumulative percentage of vehicles travelling at different speeds across all locations grouped by speed limit is shown in Figure 35.
Figure 35: Cumulative percentage (trendlines labelled by location) and 85th percentile speed at all location (the red dashed line)
It is generally accepted that the 85th percentile speed is the basis for the speed limits that are ultimately set (Montella et al., 2015; OECD/ECMT, 2006). There is a strong relationship between 85th percentile speed and posted speed limit at all survey sites, although, in some cases, the posted speed limit was found below the 85th percentile value.
Furthermore, the 90th percentile speed in both Rimbo and Kulim was observed to be more than 10 km/h above the posted limit, which means that 10% per cent of vehicles exceed this
Furthermore, the 90th percentile speed in both Rimbo and Kulim was observed to be more than 10 km/h above the posted limit, which means that 10% per cent of vehicles exceed this