| 1. |
- Fu, Jiali, 1973-, et al.
(författare)
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Optimization of Freight Charging Infrastructure Placement Using Multiday Travel Data
- 2024
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 9798350399462 - 9798350399479 ; , s. 1576-1582
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The electrification of vehicles has the potential to significantly reduce greenhouse gas emissions and environmental impacts on transportation. However, locating charging infrastructure for electric vehicles has become increasingly complex. This research examines the distribution system of a logistics firm located in Gothenburg, Sweden, and the problem of allocating shared or semi-public charging stations for their fleet of vehicles. A real-time database was set up to record trip data for the company's fleet of freight vehicles, and data from over 110,000 trips were collected from November 2021 to May 2022. The study proposes parking locations during daytime as potential sites for shared charging stations, and the problem of allocating shared charging stations is formulated as a Location Set Covering Problem (LSCP). The goal is to provide decision-making support for logistics companies transitioning to electric vehicles. The model proposed in this study can be easily adapted to other freight transport systems, facilitating a swift transition to eco-friendly freight transportation.
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| 2. |
- Gundlegård, David, et al.
(författare)
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Clustering in Before-After Studies of ITS Measures
- 2024
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 9798350399462 - 9798350399479 ; , s. 2671-2676
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Intelligent Transport System (ITS) measures are becoming more and more common on urban motorways. Different methods can be used to evaluate the effectiveness of the ITS measure, for example traffic simulation, cost-benefit analysis and before-after studies. A fundamental part of a before-after study is the choice of days for comparison of the before and after periods. Many previous before-after studies of ITS measures are based on simple methods for choosing comparable days, similar to the benchmark method used in this paper. For the chosen case study, a modified Variable Speed Limit (VSL) control strategy, we have used clustering to 1) quickly find similar days for evaluation, 2) identify type days with different characteristics and 3) identify deviating days that are non-representative for the before-after study. From the case study, it is clear that the choice of the days for evaluation are important and can generate different inference, and the clustering method provides promising results compared to the benchmark.
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| 3. |
- Postigo, Ivan, 1984-, et al.
(författare)
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Modeling Perception Performance in Microscopic Simulation of Traffic Flows Including Automated Vehicles
- 2024
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 9798350399462 - 9798350399479 ; , s. 2555-2560
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Mixed traffic with automated and human-driven vehicles interacting with one another will soon become a common reality. Microscopic traffic simulation can preemptively help assess the impact on the traffic flow dynamics as long as the tools adequately capture the differences on how automated driving systems (ADSs) drive compared to humans. In this work a modeling approach that captures differences in perception performance is proposed. While human drivers perceive through their senses and cognitive processes, ADS perceive the driving context through on-board sensors, connectivity features and software. The perception performance is described in terms of accuracy, precision, detection range, and detection delay. The model for perception is implemented in SUMO and a simulation test in a platoon shows the acceleration response affected by up to 35 % for perception errors of ≈10% which by extension will affect the traffic flow dynamics. The proposed modeling approach for perception contributes to the robustness of microscopic traffic simulation and the modeling of heterogeneous mixed traffic.
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| 4. |
- Rodrigues de Almeida, Tiago, 1996-, et al.
(författare)
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Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction
- 2023
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 24-28 Sept. 2023. - : IEEE. - 9798350399479 - 9798350399462 ; , s. 1269-1276
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Konferensbidrag (refereegranskat)abstract
- Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.
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| 5. |
- Sederlin, Michael, et al.
(författare)
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A multiple trip vehicle routing approach to ferry service network design
- 2024
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Ingår i: 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). - : IEEE. - 9798350399462 - 9798350399479 ; , s. 2784-2789
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we propose a model for joint ferry network design and scheduling in archipelago public transport systems. The model uses a multi-trip vehicle routing approach with inbound-outbound and returning trip structures where traffic requirements are implemented as window requirements. A case study is performed scheduling a full day of traffic in the southern archipelago outside of Gothenburg, Sweden. Results indicate that this approach can capture the many different stopping patterns of a line seen in real systems and produce realistic time-tables. Computational performance is shown to be highly dependent on the number of window constraints included.
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| 6. |
- Berger, Christian, 1980, et al.
(författare)
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Systematic Evaluation of Applying Space-Filling Curves to Automotive Maneuver Detection
- 2023
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Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. - 2153-0009 .- 2153-0017. - 9798350399462
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Konferensbidrag (refereegranskat)abstract
- Identifying driving maneuvers plays an essential role on-board vehicles to monitor driving and driver states, as well as off-board to train and evaluate machine learning algorithms for automated driving for example. Maneuvers can be characterized by vehicle kinematics or data from its surroundings including other traffic participants. Extracting relevant maneuvers therefore requires analyzing time-series of (i) structured, multi-dimensional kinematic data, and (ii) unstructured, large data samples for video, radar, or LiDAR sensors. However, such data analysis requires scalable and computationally efficient approaches, especially for non-annotated data. In this paper, we are presenting a maneuver detection approach based on two variants of space-filling curves (Z-order and Hilbert) to detect maneuvers when passing roundabouts that do not use GPS data. We systematically evaluate their respective performance by including permutations of selections of kinematic signals at varying frequencies and compare them with two alternative baselines: All manually identified roundabouts, and roundabouts that are marked by geofences. We find that encoding just longitudinal and lateral accelerations sampled at 10 Hz using a Hilbert space-filling curve is already successfully identifying roundabout maneuvers, which allows to avoid the use of potentially sensitive signals such as GPS locations to comply with data protection and privacy regulations like GDPR.
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| 7. |
- Kumar, Manoranjan, 1983-, et al.
(författare)
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Driving pattern classification for wheel loaders in different material handling using machine learning
- 2023
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Ingår i: IEEE Transactions on Intelligent Transportation Systems, ITSC. - : IEEE. - 9798350399462 ; , s. 283-290
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Konferensbidrag (refereegranskat)abstract
- The present paper discusses a new method of classifying kinds of material and driving stages and styles of Volvo wheel loaders (WLO). This is achieved by indirectly monitoring relevant, but usually latent variables, based on directly monitored sensors of WLOs. The continuous classifications will support Volvo’s actual objectives such as, e.g., maximizing the remaining useful life of components, fuel efficiency, and productivity.To this end, a set of WLO machines was equipped with extra sensors and collected a limited dataset with richer information. Based on this limited dataset, different machine learning (ML) methods were tested to derive and to verify the classifications. It showed that support vector machines (SVM) produced the best results: the driving styles could be classified with a test accuracy of 77% (resp, 99.5%) in the loading (resp. unloading) driving stage. Further the SVM model is also verified both theoretically to enhance the confidence in the model and experimentally with a set of additional test drivers.
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| 8. |
- Miloradović, Branko, 1987-, et al.
(författare)
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On the Design and Performance of a Novel Metaheuristic Solver for the Extended Colored Traveling Salesman Problem
- 2023
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Ingår i: IEEE Conf Intell Transport Syst Proc ITSC. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350399462 ; , s. 1955-1962
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Konferensbidrag (refereegranskat)abstract
- Intelligent transportation systems face various challenges, including traffic congestion, environmental pollution, and inefficient transportation management. Optimizing routes and schedules for efficient delivery of goods and services can mitigate the aforementioned problems. Many transportation and routing problems can be modeled as variants of the Traveling Salesmen Problem (TSP) depending on the specific requirements of the scenario at hand. This means that to efficiently solve the routing problem, all locations have to be visited by the available salesmen in a way that minimizes the overall makespan. This becomes a non-trivial problem when the number of salesmen and locations to be visited increases. The problem at hand is modeled as a special TSP variant, called Extended Colored TSP (ECTSP). It has additional constraints when compared to the classical TSP, which further complicates the search for a feasible solution. This work proposes a new metaheuristic approach to efficiently solve the ECTSP. We compare the proposed approach to existing solutions over a series of test instances. The results show a superior performance of our metaheuristic approach with respect to the state of the art, both in terms of solution quality and algorithm's runtime.
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| 9. |
- Pappaterra, M. J., et al.
(författare)
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A Review of Intelligent Infrastructure Surveillance to Support Safe Autonomy in Smart-Railways
- 2023
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Ingår i: IEEE Conf Intell Transport Syst Proc ITSC. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350399462 ; , s. 5603-5610
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Konferensbidrag (refereegranskat)abstract
- Since railways are considered among the most critical transportation infrastructures, significant research exists on the use of modern technology for their safety and security. In order to enable full autonomy of railway operations, it is not only important to guarantee safety from the control system perspective, but also to ensure that external threats of both intentional and natural origin are detected automatically. This literature review highlights the potential of Artificial Intelligence (AI) in physical infrastructure security and surveillance of autonomous railway operations. We survey AI applications to identify unauthorized access to restricted areas, as well as intelligent monitoring against natural disasters. We also discuss the use of AI to improve situational awareness, in order to analyze data from various sources to prevent incidents, provide real-time information to decision-makers, and support emergency response. Finally, we highlight the challenges to overcome, including data quality, privacy concerns, regulations, and other technical limitations.
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