Header banner MUST have an alt or title

The component interventionHeader has not been created yet or is not available for this content page type.

The component headerBackButton has not been created yet or is not available for this content page type.

General Overview

EICS Framework

EMPOWERMENTELEVATIONFOUNDATION

Region of Reference

  • AfricaAfrica
  • AsiaAsia
  • AustraliaAustralia
  • EuropeEurope
  • North-AmericaNorth-America
  • South-AmericaSouth-America
  • WorldWorld

Description

This intervention involves having access to beneficial driver information. Access to this information instils a sense of security in passengers who use e-hailing (or public transport) services. The primary source of danger in ridesharing is from their drivers, and to a lesser extent, from other passengers [6]. Not all ride-sharing services use real-time authentication, leaving fraudsters and criminals with loopholes. E-hailing services have created rapid business potential by enabling individuals to partner with other persons (drivers) to provide trips to clients [6]. However, the riders' safety and security are always at risk. Due to the ease of access and profitability of on-demand trips and e-hailing services, they can attract persons with criminal backgrounds to become driver-partners under fraudulent identities. Currently, e-hailing companies rely on government-issued credentials, such as passports and driver's licenses, to verify the identity and eligibility of their drivers. However, this verification is often conducted only once during the registration process [3].

Other public transportation services face comparable issues when it comes to passenger safety, particularly in non-government-regulated informal public transportation systems. Traffic cops are required to conduct vehicle verification on a regular basis and stopping a driver and personally validating each document is a time-consuming operation [8]. It takes a lot of time and work and determining the legitimacy of the documents is challenging.

Face recognition biometric verification is a widely used technique for real-time identification verification internationally. Artificial intelligence-based solutions for identity verification and geo-location screening assist in mitigating the danger of onboarding illegal drivers, who could jeopardize the service’s credibility. Comprehensive driver screening is critical, and a single test is insufficient in this profession.

Continuous verification by biometric screening is a viable method of mitigating fraud risk in the e-hailing market. This type of intervention would reduce crime if both drivers and riders were required to use the biometric to identify themselves prior to accessing the service; consequently, passengers would be needed to produce their government-issued IDs or biometrics as well [4].

Certain e-hailing companies have introduced selfie check-ins for drivers to verify their identities before they begin picking up passengers, buttons for reporting rides that appear to be deviating from the route, and options for rapidly contacting authorities in the event of an emergency [5]. Uber has enhanced its safety mechanisms to protect drivers from criminally prosecuted riders in South Africa, while also beefing up security measures to safeguard riders against shady drivers who may access the platform via 'rented profiles' [1]. Uber's general manager for Sub-Saharan Africa stated that the company had implemented a rider authentication function that requires new cash riders to link their rider profile to an existing Facebook account [1].

The usage of QR codes is another method for authenticating driver identification. QR codes are two-dimensional barcodes that are simple to use and create. They can link a limitless amount of data, allowing you to easily encrypt the driver's information [8]. The pandemic fuelled a surge in the use of QR codes to cut down on possible transmission [9]. They're simple to scan with a smartphone camera. As a result, QR Code technology can aid in the vehicle certification procedure [8]. You can add important information to the QR Code to improve the verification process. This information could contain the driver's contact information as well as their registration certificate.

Types of Impact

Area Impacted

  • To/from the stop/station/rank
  • Waiting for train/bus/paratransit
  • In the vehicle
  • At interchanges
This intervention would have an effect on the safety of riders and drivers when they are utilising the service, i.e. while in the car. Biometrics can be implemented on the e-hailing service's application for use prior to the user entering the vehicle to increase the user's sense of security while using the service.

Time of Day of Impact

  • Day-time travel
  • Night-time travel
  • Peak-time travel
  • Off peak-time travel
It is possible that this intervention will have a favourable influence on all travel time categories depending on the extent of implementation.

Mode Impacted

  • Bus
  • Train
  • Rideshare
  • 4 wheelers informal
  • 3 wheelers informal
  • 2 wheelers informal
  • Cycling
  • Walking
Rideshare modes have been impacted by this type of intervention, as driver information has improved. Similar systems could be developed for other public transport systems. However, railway systems do not lend themselves to this intervention.

Demographic impacted

  • Girls
  • Boys
  • Adult Women
  • Men
  • Elderly Women
  • LGBTQI+
All public transport users could be positively impacted by this intervention. Using biometrics to identify both riders and drivers each time the service is used minimizes feelings of insecurity and disincentivizes criminal activity, as criminals are immediately recognizable through this intervention. Being empowered with knowledge about the person responsible for their trip and safety can be valuable to all public transport users.

SWOT Analysis

  • Convenient and fast to use

  • Improvement in user experience: information makes it possible for users to feel secure when using the service

  • Non-transferrable. Everyone has access to a unique set of biometrics.

  • Potential for data breaches

  • Invasiveness and privacy concerns

  • The existing research on Mobile e-ID implementation barriers is limited.

  • To aid in apprehending more offenders

  • Technology is constantly evolving, making implementation simpler, quicker and cheaper

  • Bias – Machine learning algorithms must be very advanced to minimize biometric demographic bias

  • Unproven performance factors of false acceptance and rejection rates

  • Database needs to be updated frequently

  • Scammers are building their own dangerous QR codes to trick unsuspecting customers into divulging their banking or personal information.

Effectiveness

Based on the significant amount of literature reviewed, there is the confidence that this intervention can be very effective, as users’ perception of safety increases after implementation.

  • Perception by (female) passengers
  • Perception by governing bodies
  • Level of confidence in these ratings

Implementation

Implementing this intervention initially takes time, as consumers require a grace period to register new biometric information, especially when the service is first provided. The benefits are instantaneous, but this is partly contingent on how quickly users begin reporting their biometrics following implementation. The QR code is simple to use and implement and it provides immediate benefits, depending on how quickly it is implemented.

Implementation timeframe

  • 0-1 year
  • 1-3 years
  • >3 years

Timeframe to realise benefits

  • 0-1 year
  • 1-3 years
  • >3 years

Scale of Implementation

This intervention can be implemented at a station/suburb or city level.

National
Regional
City
Station or
Suburb

Ease of Implementation

This intervention takes a moderate amount of effort to implement, as it requires a moderate amount of time and some political backing.

List of References

Africa

Africa

1. Mlambo S. How e-hailing platforms are using technology to make trips safer for riders, drivers. IOL News.

Asia

Asia

2. Tang Y, Guo P, Tang CS, Wang Y. Gender-Related Operational Issues Arising from On-Demand Ride-Hailing Platforms: Safety Concerns and System Configuration. Prod Oper Manag. 2021;30(10):3481-3496. doi:10.1111/poms.13444

Europe

Europe

3. Gupta S, Buriro A, Crispo B. DriverAuth: A risk-based multi-modal biometric-based driver authentication scheme for ride-sharing platforms. Comput Secur. 2019;83:122-139. doi:10.1016/j.cose.2019.01.007