Short-Term Prediction of City Traffic Flow via Convolutional Deep Learning

Abstract

Nowadays, traffic management and sustainable mobility are central topics for intelligent transportation systems (ITS). Thanks to new technologies, it is possible to collect real-time data to monitor the traffic situation and contextual information by sensors. An important challenge in ITS is the ability to predict road traffic flow data. The short-term predictions (10-60 minutes) of traffic flow data is a complex nonlinear task that has been the subject of many research efforts in past few decades. Accessing traffic flow data is mandatory for a large number of applications that have to guarantee a high level of services such as traffic flow analysis, traffic flow reconstruction, which in their turn are used to compute predictions needed to perform what-if analysis, forecast routing, conditioned routing, predictions of pollutant, etc. This paper proposes a solution for short-term prediction of traffic flow data by using a architecture capable to exploit Convolutional Bidirectional Deep Long Short Term Memory neural networks (CONV-BI-LSTM). The solution adopts a different architecture and features, so as to overcome the state-of-the-art solutions and provides precise predictions addressing traffic flow data in cities, which are tendentially very noisy with respect to the ones measured in high-speed roads, the latter being the validation context for the majority of state-of-the-art solutions. The proposed solution has been developed and validated in the city context and data via Sii-Mobility, a smart city mobility and transport national project and it is currently in use in other contexts such as in Snap4City PCP EC, TRAFAIR CEF, and REPLICATE H2020 SCC1, and it is operative in those areas.

Data Sources and Models for Integrated Mobility and Transport Solutions

“…Such processes primarily involve data-transformation tools to extract from basic data sources a set of more usable and valuable data models, which are actually more usable for deeper analysis. The operative management processes in the blue blocks include, for example, estimations of the data (listed in pink blocks) such as: KPIs (key performance indicators) such as Sustainable Urban Mobility Indicators (SUMI) [ 119 ] and the SUMP, Sustainable Urban Mobility Plan [ 120 ], required to assess city mobility and transport management conditions/facilities; Predictions of traffic flow [ 121 ], parking lots status [ 16 ], sharing service conditions, etc., which are typically produced by some deep learning models; Anomaly detections: for example, comparing real-time conditions with respect to typical or predicted conditions and thus producing notifications, tickets for maintenance and alarms when critical conditions/events are detected; Routing, multimodal routing and conditional routing for producing routing paths by taking into account real-time traffic/environmental conditions or possible changes inside city structures due to last-minute ordinance, accidents and natural/non-natural events; Origin–destination matrices (from census data, from OBU devices, from mobile apps data, from mobile operators’ data, etc., or by data fusion): trajectories for people and vehicles, semaphores cycles and simulations, in general; Prescriptions to solve critical conditions, such as improved semaphore cycles to reduce time to across the city, changes within city viability, etc. They are typically produced by using operative research algorithms exploiting optimization models.…”

“…Predictions of traffic flow [ 121 ], parking lots status [ 16 ], sharing service conditions, etc., which are typically produced by some deep learning models;…”

Predicting Free Parking Slots via Deep Learning in Short-Mid Terms Explaining Temporal Impact of Features

“…Traffic sensors which are relevant for each garage may be one or more and they should be chosen considering the direction of travel and the most likely route to reach the garage. On the other hand, for other applications, such as routing path finding or what-if analysis in traffic reconstruction, more precise data and predictions should be used [44], [45], [46], [47].…”

Vehicular Traffic Flow Reconstruction Analysis to Mitigate Scenarios With Large City Changes

“…The short and/or long terms predictions of traffic flow data at sensors for time slots 𝑻 ̂ can be computed on the basis of historical data plus some contextual elements. For example, short terms predictions in the range of minutes and hours of traffic flow may be carried out/performed on the basis of historical [53], [54]; while midterms predictions within a day may be computed as well, by taking into account weather conditions and forecasts [55], [56], [57], [58]. Then, as to very long-term predictions, most predictive algorithms cannot produce satisfactory results with errors smaller than 15%.…”