Technology lies at the heart of new urban mobility paradigms emerging in the last few years. Among the many technologies involved in past and current mobility scenarios, satellite data plays an integral role. The mobility of the future requires precise navigation systems to ensure the proper performance of vehicles in terms of efficiency, safety, sustainability, and overall quality of the data gathered.

Public transportation is not oblivious to the new trends and challenges, so it is in this context that the fifth of Molière’s use cases was developed by researchers at the Barcelona Innovative Transportation (BIT), the research group at the Civil Engineering School in Barcelona at Universitat Politècnica de Catalunya, as a point of reference to discuss the potential uses of high accuracy data in bus systems.

The notion of accurate georeferenced data “not being necessary” in bus networks has been contested in Use Case 5, which has explored the opportunities to be exploited by the potentialities of Galileo High Accuracy Service (HAS) in bus systems and contrast them against other Global Navigation Satellite Systems (GNSS).

As such, the UPC research team developed a software tool specifically designed to analyse bus systems only through georeferenced data. This may help transit agencies reduce costs by resorting to GNSS systems to extract information, a more competitive technology compared to other available solutions nowadays.

Many of the inconveniences derived from the usage of low accurate GNSS data concern analytical tasks, as opposed to merely tracking vehicles. Statistical analysis carried out both offline and online rely on the quality of the raw data received. The number of relevant insights to be drawn solely based on accurate georeferenced information is vast: total travel times, arrival times at stops, dwell time at bus stops, headway, headway adherence, energy consumption, emission models, among others. All the operational variables mentioned above rely to varying degrees on the accuracy of the GNSS systems used. Below are only a few of the potential benefits to be drawn from using Galileo.

  • Headway assessment: The assessment of regularity by means of estimates of the headway and bunching effects through headway adherence require reliable and accurate tracking systems. Loss of information due to scattered data (i.e., unreliable GNSS system with biased and not precise observations) interfere with the estimation of time differences between consecutive vehicles. Galileo could potentially be useful in providing the necessary accuracy to undertake regularity studies.
  • Dwell times at bus stops: The error in positioning of vehicle around a bus stop leads to ambiguities regarding whether the vehicle is providing a service to passenger or simply stopped close to the bus stop. Probability distributions of dwell time at the bus stop could be empirically derived through accurate GNSS systems. By accurately estimating dwell times at bus stops, demand models can be inferred based only on GNSS data, without resorting to user information and thus guaranteeing data privacy of travellers.
  • Energy consumption: Transit operations account for a high percentage of emissions in urban settlements. In this regard, Galileo aligns with the principles of sustainability since it allows to produce more precise statistical models and causal relationships. Relationships between route slope, accelerations or speed may be more confidently estimated essentially due to the lower error values associated with Galileo. This information is particularly useful for transit operators to optimise and reduce costs related to energy consumption and society in general, since externalities such as emissions and energy consumption could be better managed.

All in all, the fifth of Molière’s use cases has pointed at the need for precise navigation systems such as Galileo within the domain of bus networks. Galileo might enjoy competitive advantage over other satellite system in:

  • Bus regularity studies: While inaccurate GNSS do not capture the full sample of observations needed to conduct analysis of headway and other variables, Gaileo’s accuracy could potentially overcome such loss of information by providing precise estimates of position and time of the vehicles.
  • Inference of operating models: Accurate demand models can be estimated through the accuracy of Galileo in understanding what type of operation a particular vehicle is carrying out based on the precise location observed.
  • Energy consumption: The accuracy of Galileo helps in precisely estimating kinematic variables and better understand energy consumption in bus networks.