3.4 Lateral Gap Maintaining Behaviour of Vehicles from Road Edge (Median)
3.4.1 Methodology for Data Collection and Extraction
In the present study, a sensor-based assembly is developed by me and Manis Kumar (a under graduate student) to measure lateral clearances maintained by vehicles from road elements (like road edge, divider, kerb footpaths, etc.). This developed assembly helps in measuring the, (i) Lateral clearance maintained by vehicles from road elements, and (ii) Speed of the vehicles. The operation is semi-automotive with very little human intervention.
Two ultra-sonic sensors are placed along the side of the road at a known distance. As a vehicle passes in front of sensor 1, the Sensor 1 gets activated and start receiving the reflected waves from the vehicle. Based on the travel time of emitted wave and reflected wave, lateral clearance of the vehicle from the sensor is calculated. A similar process is repeated at sensor 2. As sensors are places apart with a known distance, the travel time of the vehicle for the distance is determined from the time difference of actuation of both the sensors. Based on the measured travel time speed of the vehicle is calculated.
Figure 3-7 Details of the sensor assembly installed on a road
Following instruments are required in developing the sensor assembly for collecting lateral gap data from the road edge.
1. Ping Sensors: These are the low cost, non-contact and easy-to-install measurement ultra- sonic distance measuring device. They provide a range of 2cm to 300cm distance measurement.
These sensors work on sonar principle. They send out a burst of ultrasonic waves and wait for echoes to return to them. If any echo is received, the time lag (in microseconds) between the transmitted signal and received echo signal is obtained. This time lag value can be used to calculate the distance of the reflecting surface from the sensor using the following expression;
Distance (in cm) = Microseconds/58 Where Speed of sound in air = 340 m/s
Thus 1 cm corresponds to approximately 29 microseconds. Since the ultrasonic signal travels back and forth, it must be halved to receive actual distance. Thus microseconds divided by 58 (=29X2) gives the distance of the object from the sensor in cm.
Figure 3-8 Represents the (a) Ultrasonic ping sensor with all its connections (b) Arduino Uno Micro controller board
2. Arduino UNO: The Arduino Uno is a microcontroller board based on the ATmega328. It has 14 digital input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz ceramic resonator, a USB connection, a power jack, an ICSP header, and a reset button.
3. SD Card: A micro-SD card is used with the Arduino UNO board to record the data. Two web cameras have been connected to the laptop for taking the snapshots. Whenever a vehicle passes in front of the sensor 1 the first sensor gets activated and starts recording the distance data with respect to the time stamp. Then as soon as the sensor activates the camera got activated and it starts taking the snapshot of the section. Whenever any non-zero values are coming in sensor, the camera gets activated. This instruction is given through a code written in MATLAB program. The snap shots of each camera are being stored on the laptop with their time stamp. A similar procedure is followed for sensor-2 also. The Figure 3.9 shows the full set-up layout.
(a) (b)
Figure 3-9 A complete layout of Sensor assembly with two cameras
This assembly solves the problem of manual data collection from videos which is a time- consuming process. The vehicle type identification is easier with this model. The camera is properly synchronized with the sensor. Hence the error due to manual data synchronization can be reduced through this model. To counter the problem of limited battery backup, extra battery packs can be kept for the laptop model being used for data acquisition. Another solution is maybe keeping a 12-V lead acid battery along with portable 200 W inverter. These items can be carried in a small vehicle to the data collection site. The complete working procedure of this set-up is shown in flow chart in Figure 3.10.
To avoid the garbage or unwanted data, a limit has been fixed through the program for the number of data. The sensor will take only 5 data for each vehicle passing in front of it. Hence the corresponding camera will also take five number of snapshots corresponding to each sensor reading. Then by comparing the sensor reading and snapshot it will be easy to identify the vehicle type.
The sensor assembly has been so designed that it measures the lateral clearances with an accuracy of ±5 cm. Thus, the developed assembly aids in measuring the following;
1. The lateral clearance maintained by vehicles from road elements.
2. Speed of different vehicles in the traffic mix
Since we are using the sensor time stamp for speed calculation, there might be an error due to the time lag between consecutive vehicle detections. The data collection frequency of the sensors is 30-35 Hz. Therefore, the time gap between readings varies between 0.0286-0.033 seconds. The maximum error will correspond to the maximum time lag, i.e. 0.033 seconds. For reliability condition, let maximum time lag of 0.035 seconds. If the distance between the two sensors be D0 (m) and the speed of the vehicle be v0 (m/s), the vehicle should take time t0 to
Sensors Arduino Computer
Figure 3-10 Presents the complete working procedure of the Sensor Assembly
cross this distance at the original speed. Hence, the time to cross the sensor, t0 = D0/v0.
Considering the error (δt) as 0.035 seconds, recorded speed will be v’ = D0/(t0-δt).
Hence, the percentage of error (%) = (v’-v0)*100/v0. Thus, the error in measured speed will depend upon;
Sensor distance (D0)
True speed (v0) of the passing vehicle.
Figure 3-11 Maximum percentage error which may occur in speed measurement using the proposed sensor assembly
From Figure 3.11, it can be observed that as the distance between sensors increases the error in the speed of vehicles decreases. For a distance of 20m between the sensors, then 5% error is detected for a vehicle travelling at 100 km/h. Hence, in the present study, the 20m distance has been used between the sensors while collecting the data using the sensor assembly.
The sensor assembly is placed along the edge of the road with all the proper setup as mentioned above. The sensors are fixed at such a height where it can detect all type of vehicles (motorized 2Ws, 3Ws, Car, Bus, and Truck etc.) in the mixed traffic stream. The height of placement of sensor is based on the height of different vehicles, i.e. the seat height of 2Ws and the chassis height of different type of cars, buses and trucks. A height of 0.8 meter from the carriageway is fixed for the sensor placement at the road boundary.
This assembly helps in large-scale data collection of the lateral clearances maintained by the vehicles from the road elements. Data was collected on straight mid-block sections of roads with different median facilities. The impact of the different lane positions (such as the Median Lane, ML and Shoulder Lane, SL) has been analysed for all sections on average travel speed.
The lateral clearance of each vehicle type from the road edge of SL/ML is calculated.
0 5 10 15 20 25 30
0 20 40 60 80 100 120
% Error
Speed (km/hr)
%Error-Speed curves
Do = 10 m Do = 5 m
Do = 20 m