Smartphone‐based crowdsourcing for position estimation of public transport vehicles

SVM‐based hybrid approach for corridor‐level travel‐time estimation

“…In this approach, the aim is to construct the posterior PDF p ( x ( k + 1) | Z ( k )) of the state vector x ( k + 1) given the measurements Z ( k ). The detailed formulations for PF mechanism are given in studies [43, 51].…”

“…The main advantage of a PF is that it does not require the process and sensor noise to be Gaussian distributed. The PFs are widely used for various application like real‐time travel‐time estimation [40–43], studies related to traffic parameter estimation [21, 44–49] and vehicular tracking [40, 48, 50, 51]. PFs are also used for estimating the probability density functions (PDFs) of the system states [52].…”

Crowd intelligence for sustainable futuristic intelligent transportation system: a review

“…Dienstl et al further claimed that the demand–responsive transportation model can track incidents more accurately in the city. Mukheja et al [115] developed a real‐time positioning method by using crowdsourced positioning data from smartphone GPS. The authors claimed that this location information by using the crowdsourced method can replace the traditional location systems.…”

IoT Based Bus Arrival Time Prediction Using Artificial Neural Network (ANN) for Smart Public Transport System (SPTS)

“…Accessing this location data from servers will be facilitated by Representational State Transfer (REST) APIs which users can access through the android application, SMS or web-portals. Enhanced particle filter algorithm [3] takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data to track the position of the bus.…”