| 1. |
- Chen, Haoye, et al.
(författare)
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Pick-Up and Delivery Problem for Sequentially Consolidated Urban Transportation with Mixed and Multi-Pupropse Vehicle Fleet
- 2022
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Ingår i: Journal of Advanced Transportation. - : Hindawi Limited. - 0197-6729 .- 2042-3195. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- Different urban transportation flows (e.g., passenger journeys, freight distribution, and waste management) are conventionally separately handled by corresponding single-purpose vehicles (SVs). The multi-purpose vehicle (MV) is a novel vehicle concept that can enable the sequential sharing of different transportation flows by changing the so-called modules, thus theoretically improving the efficiency of urban transportation through the utilization of higher vehicles. In this study, a variant of the pick-up and delivery problem with time windows is established to describe the sequential sharing problem considering both MVs and SVs with features of multiple depots, partial recharging strategies, and fleet sizing. MVs can change their load modules to carry all item types that can also be carried by SVs. To solve the routing problem, an adaptive large neighborhood search (ALNS) algorithm is developed with new problem-specific heuristics. The proposed ALNS is tested on 15 small-size cases and evaluated using a commercial MIP solver. Results show that the proposed algorithm is time-efficient and able to generate robust and high-quality solutions. We investigate the performance of the ALNS algorithm by analyzing convergence and selection probabilities of the heuristic solution that destroy and repair operators. On 15 large-size instances, we compare results for pure SV, pure MV, and mixed fleets, showing that the introduction of MVs can allow smaller fleet sizes while approximately keeping the same total travel distance as for pure SVs.
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| 2. |
- Jenelius, Erik, Docent, 1980-
(författare)
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Traveller Recurrence and Inter- versus Intratraveller Speed Variability : Analysis with Bluetooth Data
- 2022
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Ingår i: Journal of Advanced Transportation. - : Hindawi Limited. - 0197-6729 .- 2042-3195. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a linear mixed model of route speed distributions that separates the variability into an intertraveller component, consistent across days and time intervals for each recurrent traveller, and an intratraveller component representing uncertainty. The intratraveller variability corresponds to travel time uncertainty, while the total variability is typically captured by empirical measurements and used in travel time reliability assessments. The intratraveller and the total variability differ if there are systematic differences in speed between different recurrent travellers. The paper also investigates to what degree vehicles traversing a route during the morning or evening peak over multiple days are recurrent travellers. Using data from Bluetooth and Wifi sensors on 26 routes in Stockholm, Sweden, over a three-month period, we find that the traveller recurrence is higher towards the city in the morning peak and out from the city in the afternoon. Model estimation results show that the relative intratraveller variability is also significantly higher in the commute direction (towards the city in the morning and out from the city in the afternoon) and on routes with high congestion levels. The relations revealed in this paper may be used to estimate the relevant intratraveller variance based on the total variance and readily available route attributes. Without this correction, the costs associated with travel time variability may be overestimated.
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| 3. |
- Liu, Zhengfa, et al.
(författare)
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Mixed Event-Frame Vision System for Daytime Preceding Vehicle Taillight Signal Measurement Using Event-Based Neuromorphic Vision Sensor
- 2022
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Ingår i: Journal of Advanced Transportation. - : Hindawi Limited. - 0197-6729 .- 2042-3195. ; 2022
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Tidskriftsartikel (refereegranskat)abstract
- An important aspect of the perception system for intelligent vehicles is the detection and signal measurement of vehicle taillights. In this work, we present a novel vision-based measurement (VBM) system, using an event-based neuromorphic vision sensor, which is able to detect and measure the vehicle taillight signal robustly. To the best of our knowledge, it is for the first time the neuromorphic vision sensor is paid attention to for utilizing in the field of vehicle taillight signal measurement. The event-based neuromorphic vision sensor is a bioinspired sensor that records pixel-level intensity changes, called events, as well as the whole picture of the scene. The events naturally respond to illumination changes (such as the ON and OFF state of taillights) in the scene with very low latency. Moreover, the property of a higher dynamic range increases the sensor sensitivity and performance in poor lighting conditions. In this paper, we consider an event-driven solution to measure vehicle taillight signals. In contrast to most existing work that relies purely on standard frame-based cameras for the taillight signal measurement, the presented mixed event/frame system extracts the frequency domain features from the spatial and temporal signal of each taillight region and measures the taillight signal by combining the active-pixel sensor (APS) frames and dynamic vision sensor (DVS) events. A thresholding algorithm and a learned classifier are proposed to jointly achieve the brake-light and turn-light signal measurement. Experiments with real traffic scenes demonstrate the performance of measuring taillight signals under different traffic conditions with a single event-based neuromorphic vision sensor. The results show the high potential of the event-based neuromorphic vision sensor being used for optical signal measurement applications, especially in dynamic environments.
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