As technology continues to advance at break-neck speed, the transport sector has been a big beneficiary of this evolution even as various technologies tailor-made for the field continue to be piloted and rolled out.
Technology has made it possible to collect a wide range of transportation data including travel time, speed, the passage of every vehicle, and time gaps.
This advancement has culminated in phasing out inefficient methods of performing tasks and provided a platform for smart ways to solve transport challenges.
For instance, transportation Artificial Intelligence (AI) systems in the current age are being used to:
- Predict paths used by cyclists and pedestrians, hence reducing accidents and environmental pollution.
- Enhance public safety by predicting crime through tracking data.
- Study traffic patterns including causes of congestion and delays.
- Provide evidence-based basis for corporates to make decisions like forecasting and accurate prediction.
It was not always like this though.
For context purposes, it is important to highlight that when collecting transport data in the traditional manual way, transport authorities and companies had to spend a lot of time and resources on tasks that are considered light work today.
In traditional manual traffic data collection, for instance, authorities had to have one person on each lane in order to boost accuracy.
Now fast forward to an interesting statistic.
One billion surveillance cameras will be deployed across the world by the end of 2021 up from 770 million surveillance cameras in 2019 representing a 30% increase, according to a report compiled by research firm IHS market as first reported by the Wall Street Journal.
It is not humanly possible to review all that data with human eyes, hence the proliferation of various technologies built to solve such challenges.
Quite the evolution right?
This prompts a necessary discussion — The Evolution of Transport Data Collection Methods.
- Traditional Transport Data Collection Methods
- Travel Monitoring
- Evolution Into Modern Transport Data Collection Methods
- What Does The Future Hold?
- Related Posts
Traditional Transport Data Collection Methods
There are two broad categories of traditional transport data collection methods, namely:
- Travel monitoring
A survey is defined as a close look at something in an effort to examine it.
Under the surveys banner, there are multiple traditional transport data collection methods as outlined below.
- Household travel surveys
- Workplace surveys
- Stated preference surveys
- Transit on board (TOB) ridership surveys
- Commercial vehicle surveys
Household Travel Surveys
This is the kind of survey where people living under the same roof, mostly families, are questioned and give feedback on their transportation data. The questions mainly revolve around: Preferred mode of travel, Travel movements, and Times when the family or individuals within the family travel.
In this type of survey, physical, telephone, and mail interviews can be used.
Small sample sizes are welcomed mostly for calibrating trip distribution and trip generation models whereas larger sample sizes are needed for calibrating mode choice models accurately.
These are surveys conducted at organizations where people work. They are often used to gather crucial detailed information such as specific reasons behind a trip.
These surveys churn out disaggregate data that is used to examine trip attraction rates.
However, one downside of workplace surveys is that they are expensive to carry out. Some of the reasons why workplace surveys are expensive to carry out include:
- They are time-consuming
- Workplace surveys are more detailed, meaning they require more manpower and materials to conduct
- Workplace surveys require clearance, hence not as straightforward as other traditional data collection methods
Stated Preference Survey
In a stated preference survey, a respondent is asked to make a travel decision that visualizes the available substitutes and their characteristics.
More data variety is collected in stated preference surveys compared to workplace surveys.
However, it must be noted that stated preference surveys do not portray actual travel behaviour but rather respondents give an account of how they would like to behave.
Transit on Board (TOB) Surveys
Transit on Board Surveys acquire data through intercepting passengers travelling in a surveyed transit vehicle.
ToBs have traditionally been counted on by public transport operators to get a rough idea of ridership profiles.
Inferences from ToBs have also been used by travel demand modellers to create trip tables, which are in turn used to validate travel models and to enhance household survey data for the establishment of mode choice models.
One interesting aspect of ToBs is that they are self-administered and are short enough to complete during the course of the transit.
Commercial Vehicle Surveys
Commercial Vehicle Surveys were traditionally used to gather information on truck trips made within a particular region.
Because of the confidential nature with which data on commercial trucking is handled and the difficulty in establishing the population due for a survey, very few extensive Commercial Vehicle Surveys have been completed in recent years.
Besides surveys, there exist traditional ways to collect transportation data.
Traditional Traffic Count Techniques
There are three basic types of equipment used to effectively collect traffic data, namely:
- Weigh-in-Motion (WIM) devices
- Vehicle Classification recorders
- Traditional traffic volume counters
Weigh-in Motion (WIM) Devices
Weigh-in-Motion devices are technological gadgets used to record and capture important data such as gross vehicle and axle weights as vehicles pass over a measurement site.
Both permanent and portable WIM devices can collect data on Time, Date, Speed, Vehicle Lengths, Spacing, and Axle Weights.
The portable WIM works by using a combination of a capacitance weigh pad and two loops to collect data.
On the other hand, the permanent model uses a combination of one or two loops together with one or more axle weight sensors to collect data.
Vehicle Classification Recorders
Normally portable in nature, these recorders use two-axle sensors to sort vehicles into 13 Federal Highway Administration (FHWA) categories. Data recorded includes time, date, axle spacing, speeds, and the number of vehicles per axle.
Traditional Traffic Volume Counters
Traffic counters are devices used to count, measure or classify the speed of vehicular traffic on a given road.
Traffic volume counters can be either permanent or portable.
These counters use a single axle sensor and may incorporate cumulative counts recorded electronically, on printed paper, or on punch tape.
The permanent traffic counters use one single inductive loop that senses passing vehicles and records data on speed and vehicle lengths for a given amount of time.
Evolution Into Modern Transport Data Collection Methods
As captured in the first paragraph, advancement in technology has led to smarter ways of collecting transport data.
Some of these modern transport data collection methods include but are not limited to:
- Electronic ticketing and automated trip payment
- Transport Smart Cards
- Global Positioning System (GPS)
- General Transit Feed Specification (GTFS) Data
- Video Imaging Technology
- License Plate Matching
- Automated Vehicle Identification (AVI)
Electronic Ticketing and Automated Trip Payment
With electronic ticketing systems, people are able to select and buy tickets. On the other hand, automated trip payment machines make it possible for people to access a vehicle or transportation system quickly without the presence of staff dedicated to collect fares.
It is important to note that this technology provides key sensitive data such as the total number of riders during a trip, total fare collected during a trip, and zonal ridership information.
In Phoenix, United States passengers are allowed to pay fares via plastic money (credit card) alongside a designated BusCard Plus transit system card.
Transport Smart Cards
In 2004, Istanbul, one of the biggest cities in Turkey, got rid of an inefficient public transport system and put into service the smart card known as Istanbulkart in the city’s transportation system.
The Istanbulkart tapped during entry or exit resembles a credit card and bears a unique serial number. The card can be personalized to specific groups such as civil servants, the elderly, and students.
The result was reduced fraud, automated fare collection, reduced maintenance costs, and reduced time of travel. The Istanbulkart enhanced the city’s reporting and data collection.
Data collected by the cards such as departure and arrival time, boarding and alighting locations, and mode of transport, is transferred to the data automation server, which can be used as tangible evidence in urban planning.
Global Positioning System (GPS)
As many would have heard by now, Global Positioning System is a space-based radio navigation system that allows users positioned on land, air, and sea to establish their exact location, time, and velocity anywhere across the world 24 hours a day.
GPS is the primary technology used by tracking companies to locate vehicles and monitor fleets.
Currently, GPS has advanced so much that it is now used to collect travel time data.
Aggregated GPS data can be used to deduce an overall picture of traffic patterns and flows.
Mobile Phones & General Transit Feed Specification (GTFS)
General Transit Feed Specification (GTFS) is a data specification that enables public transit authorities to publish their data in a format that can be consumed and analyzed via various software applications by thousands of public transport service providers.
GTFS is designed to be simple to enable small agencies to adopt the standard with the same ease as big agencies. Consequently, GTFS uses Comma-Separated Values (CSV) files.
TransitWand is a mobile or tablet application used to collect GPS route and stop data, most notably passenger boarding and alighting data, which can be processed via the GTFS editor besides other analysis functions.
TransitWand is also used as a tool to record the composition of public transport networks in unmapped cities.
Video Imaging Technology
This is smart camera technology that uses a video-imaging detection system to capture and record transport data.
This technology can distinguish between typical rush-hour congestion and irregular incidents.
This technology is also useful for recording traffic speeds, volumes and vehicle classifications.
The California Department of Transportation uses this technology to collect and analyze transportation data and make informed planning decisions.
Video imaging technology can also be used to count the number of people in public transport vehicles and stations to obtain occupancy data accurately.
License Plate Matching
This technology involves the use of a minimum of two cameras placed at different sections on a road. The images of license plates captured from downstream vehicles are contrasted with upstream vehicles to establish travel time and the speed at which vehicles are moving in that segment.
To enable this, one camera is mounted on each lane.
The plate numbers can either be manually matched which is expensive and time-consuming or via a machine vision system that automatically assesses the number plates.
Automated Vehicle Identification (AVI)
Automated Vehicle Identification (AVI) is a technology that identifies a car as it passes a Radio Frequency Identification (RFID) reader.
One advantage that AVI has is that it can collect information regardless of lighting conditions and travel speeds which inhibit other technologies such as video imaging.
This technology furnishes the user with real-time travel information, which aids in improving relief strategies and response times.
The Texas Department of Transport uses AVI technology to collect transport data by installing the AVI tech on its freeways and HOV lanes.
What Does The Future Hold?
Technology has advanced so much that it is now possible to stop accidents on a road before they happen. That is made possible by AI technology that can, for instance, predict paths used by cyclists and pedestrians.
However, AI is not limited to stopping accidents since it also helps cut down on environmental pollution, hence promoting greener living in the process of cutting down emissions among other benefits.
The obvious question that arises is, if transportation technology is this advanced right now, what does the future hold?