Understanding travel behavior is critical for an effective urban planning as well as for enabling various context-aware service provisions to support mobility as a service (MaaS). Both applications rely on the sensor traces generated by travellers’ smartphones. These traces can be used to interpret travel modes, both for generating automated travel diaries as well as for real-time travel mode detection. Current approaches segment a trajectory by certain criteria, e.g., drop in speed. However, these criteria are heuristic, and, thus, existing approaches are subjective and involve significant vagueness and uncertainty in activity transitions in space and time. Also, segmentation approaches are not suited for real time interpretation of open-ended segments, and cannot cope with the frequent gaps in the location traces. In order to address all these challenges a novel, state based bottom-up approach is proposed. This approach assumes a fixed atomic segment of a homogeneous state, instead of an event-based segment, and a progressive iteration until a new state is found. The research investigates how an atomic state-based approach can be developed in such a way that can work in real time, near-real time and offline mode and in different environmental conditions with their varying quality of sensor traces. The results show the proposed bottom-up model outperforms the existing event-based segmentation models in terms of adaptivity, flexibility, accuracy and richness in information delivery pertinent to automated travel behavior interpretation.
Transport mode information is essential for understanding people’s movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other’s limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.
R. Kutadinata, R. Thompson, and S. Winter, “Cost-efficient Co-modal Ride-sharing Scheme Through Anticipatory Dynamic Optimisation,” in Proceedings of the 23rd ITS World Congress, 2016.
This paper considers the vehicle routing problem when dealing with a co-modal demand-responsive transport service. The vehicles in the service are shared among two modes of customers, passengers and goods deliveries. In particular, this paper develops a conceptual model in order to explore the performance of such a service with two different optimisation algorithms, namely deterministic re-optimisation and the Multiple Scenario Approach (MSA). An important contribution of this work is the formulation of the co-modality as a pick-up and delivery problem with time windows (PDPTW). In addition, the effect of using various constraints and penalty functions in the optimisation formulation will be investigated. The experiment will be carried out in a vehicle routing simulation developed in MATLAB by using a demand scenario obtained from the Victorian Integrated Survey of Travel and Activity (VISTA) data. In the model, the performance of the algorithms is measured by the operating cost, the number of customers whose time-window constraints are violated, and the average wait and detour time.
The University of Melbourne had a booth at the 23rd ITS World Congress, held in Melbourne, which we helped organised. The purpose of the booth is to showcase the ITS research capabilities at the university and attract interest from industry for potential collaboration.
The booth had many visitors and can be considered a success. There are some discussions for potential projects resulting from the engagement.
A flyer has been produced for this purpose, which can be downloaded here. Please do not hesitate to contact us if you have any inquiries.
In addition, Ronny presented his findings on anticipatory algorithm for shared passenger-freight DRT services, the details of which can be found here.
This paper won the Best Vision Paper award and the CCC Blue Sky Ideas award. Read more in the CCC website.
Since in many cities transport infrastructure is operating at or beyond capacity, novel approaches to organize urban mobility are gaining attraction. However, assessing the benefits of a measure that has disruptive capacity in a complex system requires a carefully designed research. This paper takes a recent idea for urban mobility – flexible road trains – and illustrates the computational and research challenges of realizing its full potential and describing its social, ecological and economical impact.
Ridesharing is an emerging travel mode that reduces the total amount of traffic on the road by combining people’s travels together. While present ridesharing algorithms are tripbased, this paper aims to achieve signicantly higher matching chances by a novel, activity-based algorithm. The algorithm expands the potential destination choice set by considering alternative destinations that are within given space-time budgets and would provide a similar activity function as the originals. In order to address the increased combinatorial complexity of trip chains, the paper introduces an efficient space-time filter on the foundations of time geography to search for accessible resources. Globally optimal matching is achieved by binary linear programming. The ridesharing algorithm is tested with a series of realistic scenarios of different population sizes. The encouraging results demonstrate that the matching rate by activity-based ridesharing is signicantly increased from the baseline scenario of traditional trip-based ridesharing.
R. Das and S. Winter, “A Neuro-Fuzzy based Hybrid Intelligent Framework for Transport Mode Detection,” in Proceedings of the 6th International Workshop on Mobile Entity
Localization, Tracking and Analysis (MELT), 2016.
Understanding transport mode detection, is important to transport planning and movement behavior analysis. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classication. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format with more exibility, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel multi-layered hybrid intelligent framework is developed by integrating a fuzzy logic and a machine learning approach to complement each other’s limitations. Thus in this paper an intelligent fuzzy logic-based near-real time mode detection framework is presented that can perform comparable to state-of-the-art machine learning approaches by bridging the trade-off between explanatory power and prediction accuracy of the models. The results demonstrate that the proposed framework has achieved the aim in the context of near-real time transport mode detection.
S. Gupta, M. Vassardani, and S. Winter, “Conventionalized gestures for the interaction of people in traffic with autonomous vehicles,” in Proceedings of the 9th International Workshop on
Computational Transportation Science, 2016.
The first autonomous vehicles are already tested in the public traffic . The rapid development in bringing this technology on roads attracts growing attention of research in the human interaction with autonomous vehicles. This paper focuses on the interaction of other road users with autonomous vehicles. These road users may be pedestrians who negotiate their right of way, other human drivers sharing the same road, or human traffic control officers. In order to learn about these road users in general, this paper aims to identify first the formalized hand signals applied by officers. The paper answers the question whether there is a general and universal language to interact with traffic. If so, then future work can identify elements of this universal language in the gestures of other road users, and facilitate an understanding between them and autonomous vehicles.