Abstract
This paper studies the joint reconstruction of traffic speeds and travel times by fusing sparse sensor data. Raw speed data from inductive loop detectors and floating cars as well as travel time measurements are combined using different fusion techniques. A novel fusion approach is developed, which extends existing speed reconstruction methods to integrate low-resolution travel time data. Several state-of-the-art methods and the novel approach are evaluated on their performance in reconstructing traffic speeds and travel times using various combinations of sensor data. Algorithms and sensor setups are evaluated with real loop detector, floating car and Bluetooth data collected during severe congestion on German freeway A9. Two main aspects are examined: 1) which algorithm provides the most accurate result depending on the used data and 2) which type of sensor and which combination of sensors yields highest estimation accuracy. Results show that, overall, the novel approach applied to a combination of floating-car data and loop data provides the best speed and travel time accuracy. Furthermore, a fusion of sources improves the reconstruction quality in many, but not all cases. In particular, Bluetooth data only provide a benefit for reconstruction purposes if integrated subtly.
Highlights
For various applications in traffic engineering, it is fundamental to know about the traffic conditions on a road stretch with high certainty and sufficient spatio-temporal accuracy
2) The Inverse Mean Average Error (IMAE) has a higher variance than the Mean Absolute Percentage Error (MAPE). 3) Some algorithms perform best with respect to the IMAE in a scenario but are outperformed with respect to the MAPE
5) The “Phase-Based Smoothing Method (PSM)-W” performs significantly better in IMAE and MAPE in all scenarios that involve BT data
Summary
For various applications in traffic engineering, it is fundamental to know about the traffic conditions on a road stretch with high certainty and sufficient spatio-temporal accuracy. A complete representation of traffic conditions is especially crucial for understanding traffic flow, for the effectivity analysis of control measures and for training data-driven prediction models. The retrospective analysis often focuses on average vehicle speeds per time and space interval on a road since this provides benefits such as enabling the deduction of travel times for road users, providing jam tail warnings (Rempe et al, 2017b) aiming at the reduction of rear-end collisions at jam tails, etc. Using current sensor technology, average vehicle speeds are not measured for all times and places on a road stretch. Various types of sensors are available that provide traffic-related data at different times for different places. Raw sensor data must be processed in order to determine an accurate reconstruction of traffic conditions
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