Infrastructure Engineering - Research Publications
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ItemPublic transportation-based crowd-shipping initiatives: Are users willing to participate? Why not?(Elsevier, 2024-04)An emerging stream of Crowd-Shipping (CS) solutions focuses on existing momentum in Public Transportation (PT) to ship viable delivery packages by PT passengers. Few studies have explored the package delivery acceptance behavior of passengers engaged in PT-based CS initiatives while passengers’ behavioral intention to participate (i.e., engage) is not studied. It is requisite that newly introduced CS platforms explore their potential crowdshippers’ behavior on intention to participate and set efficient marketing strategies. Given survey data collected from 2208 PT passengers in Sydney metropolitan area, this study explores the intention of PT passengers as crowd-shippers to participate in PT-based CS initiatives, as well as prohibiting factors in way of participation. Accordingly, a binominal logit model is developed whereby the variables impacting the intention to participate are identified. Then, using an inductive thematic analysis, 917 reasons (text responses) for not participating are scrutinized, and the prohibiting factors are identified and categorized. Considering demographic and socio-economic characteristics of the respondents, the study reveals to what degree passengers with different characteristics are sensitive to prohibiting factors. This research provides several practical insights that can assist in successfully defining, launching, and advertising a new PT-based CS initiative. As a key finding, it is observed that women, full-time employees, elderly, retirees, and low-income PT passengers hardly participate, while the youth, individuals with a positive attitude towards sustainable freight initiatives, and those who experienced working with parcel lockers would participate with a higher probability. Moreover, it is observed that factors relating to time availability/flexibility and physical health condition/importance of passengers are much more important than the compensation level for passengers to accept to participate in PT-based CS initiatives.
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ItemLast-Mile logistics with on-premises parcel Lockers: Who are the real Beneficiaries?(Elsevier, 2024-03)Researchers have investigated various planning aspects of public Parcel Lockers (PLs), but little is known about the wider benefits of on-premises parcel lockers (OPLs). More recently OPLs have received attention as a solution to address the problem of failed deliveries, as well as to provide a safe and secure delivery experience to customers. Although OPLs provide enhanced convenience for residents, a major impediment towards their adoption is the high capital and ongoing costs. Currently, suppliers of OPLs require individual buildings to fully cover the costs, leading to low uptake among building managers and residents. This study is the first to ruminate that the benefits of such systems expand beyond residents, with carriers and local governments being key beneficiaries. Using a real-world case study and in-field observations, this study provides analytical estimates of the value of OPLs for buildings, as well as carriers and local governments in terms of operational and external cost savings. A probabilistic algorithm is developed to simulate the delivery time components to buildings and measure the benefits for individual stakeholders. We further applied the model to a case study in the City of Melbourne, to evaluate how a carrier's market share, and building size and practices for managing failed delivery could determine the value of OPLs. The simulation results reveal that the direct benefits of OPLs are first characterized for the buildings, followed by carriers and local governments. We further describe how appropriate funding and pricing mechanisms, involving both carriers and buildings, could further facilitate commercial viability of OPLs for smaller buildings.
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ItemLightweight traffic anomaly detection: A case study with SCATS volume data of Melbourne(Australasian Transport Research Forum, 2023)In this paper, we evaluate performance of an anomaly detection framework with real traffic count data collected by SCATS (Sydney Coordinated Adaptive Traffic System) loop detectors in Melbourne. The goal is to detect anomalous daily volume profiles within temporally large historical traffic data utilizing a lightweight and parameter-free approach and use it for live applications. To achieve this, daily volume profiles are first compressed into two dimensions benefiting from the Principal Component analysis (PCA). Then, a parameter-free version of DBSCAN is applied to the data with unique days of the week. Results from more than 20 different locations in Melbourne are fully visualized and the advantages and disadvantages of the method are discussed. We found that, with this approach, anomalous volume profiles can be accurately detected in a wide range of spatiotemporal data without any pre-training, parameter setting, or using complex learning methods.
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ItemTesting request prioritization strategies to improve the quality of a shared autonomous vehicles service: A Melbourne case study(Australasian Transport Research Forum, 2023)Shared autonomous vehicles (SAVs) have the potential to revolutionize urban transport by offering a mobility service that combines the benefits of autonomous vehicles and ride-hailing systems. However, one of the main limitations of these services is the handling of all request types in a singular way, which can increase biases towards a fixed type of passengers who have different properties and accessibility-based constraints. This study proposes a prioritization approach based on different request properties and the urgency of the request. The study uses the MATSim simulator to evaluate the prioritization schemes and implements five different scenarios to assess the collective and individual effects of the schemes. The case study focuses on Melbourne Metropolitan Area, and the prioritization is done based on the existing public transport (PT) service and pre-calculated SAV demand of the network. The study considers different performance measures, such as service efficiency, externalities, and provision equity, to estimate the benefits of the prioritization. The results indicate that prioritization can improve overall equity by spreading wait times evenly across the network. The prioritization approach improves the service with more riders finding the service attractive, resulting in more served rides with lower average vehicle kilometers traveled per ride. In addition, the study shows that the PT mode share is increased in multiple scenarios, demonstrating the positive effect of considering accessibility when prioritizing requests.
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ItemWhat influences passenger’s arrival rate at stops in Melbourne: WASEA-LSTM: a novel deep learning model leveraging multi-source data fusion(ATRF, 2023)Public transportation demand plays a crucial role in service planning and operation. Accurate prediction of passenger arrival rates at transit stops allows transportation planners and operators to optimize resources and improve service efficiency. Current methodologies primarily focus on weather's impact in the aviation industry, supply dynamics, and arrival time prediction, while overlooking its influence on public transport demand variation. This study addresses these gaps by designing a deep neural network model that can predict public transit demand, using large-scale datasets from multiple sources in Melbourne, Australia. We propose a novel deep learning architecture called Wasea-Lstm (Weather-Aware Smart Exponential Activation LSTM) that captures spatial, temporal, and external correlations for passenger arrival rate prediction at tram stops. The model is trained and tested on integrated datasets from automatic fare collection (AFC), automatic passenger count (APC), and weather data over a period of three months. Results show that the Wasea-Lstm model significantly outperforms benchmark models, including gradient boosting machine (GBMR) and multi-layer perceptron (MLP) regression by 15% and 6% in R2 metric, respectively. The feature importance ranking reveals that stop location, time of the day, temperature, and humidity are the key influencers of passenger arrival behaviour in Melbourne. Overall, this study contributes to the development of a model that accounts for multi-dimensional, high-resolution determinants of passenger demand using large-scale datasets from real world. The proposed Wasea-Lstm architecture shows exceptional performance in precisely forecasting stop-level demand for one of Melbourne's largest tram routes. Moreover, its applicability extends seamlessly to all routes within the network.
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ItemModeling package delivery acceptance in Crowdshipping systems by Public Transportation Passengers: A latent class approach(Elsevier BV, 2024-04-04)The subject of this research is to model the probability of accepting package delivery tasks in Crowd-Shipping (CS) systems focusing on shipping packages by Public Transportation (PT) passengers. Real-world implementation of such a PT-based CS initiative requires a proper grasp of the behavior of crowd shippers, package receivers, and senders, among which crowdshippers are at the center of attention. Given survey data collected from 2,208PT passengers in the Sydney metropolitan area, this study constructs a model for package delivery task acceptance among PT passengers who express a willingness to participate as crowdshippers. A Latent Class (LC) choice model is developed and estimated to explore different classes of participant characteristics and preferences for accepting CS delivery tasks, under different levels of offered incentive, package weight, and required detour distance at the destination. As a key finding, the model identifies three classes, namely “leisurely”, “avid”, and “skeptical” users with class sizes of 19%, 53%, and 28%, respectively. Leisurely users tend to be majorly employed with relatively higher ages and lower PT trip frequency per month. This class shows a potential desire to take CS tasks as long as they are not logistically burdensome. Avid users, forming the largest class of the population, are mostly younger PT passengers who are less sensitive to task difficulty and might accept even CS tasks with minimum incentives (around AU$3). Skeptical users, displaying high PT trip frequency per month and high income, are unwilling to accept the CS tasks unless get compensated well.
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ItemNo Preview AvailableTraffic Anomaly Detection: Exploiting Temporal Positioning of Flow-Density Samples(Institute of Electrical and Electronics Engineers (IEEE), 2023)It is of paramount importance to detect traffic data anomalies in a real-time manner as it helps efficient traffic control and management. Several unsupervised anomaly detection algorithms are proposed previously in the literature; however, lack of proper ground truth labels for traffic data has been always a substantial barrier to deploy and evaluate them. In this paper, we introduce a concept named Temporal Positioning of Flow-Density Samples (TP-FDS) that can be used by domain experts for fast and reliable traffic data labeling. We mathematically show that deviations in two-dimensional TP-FDS completely reflect point and subsequence anomalies previously defined in the literature of time series data. Furthermore, benefiting from this concept, we propose a novel anomaly detection framework with the help of Fast Angle Based Outlier Detection (Fast-ABOD) to be used for traffic data. Extensive data labeling experiments are conducted with the opinions of 20 different experts. Implementation of several machine learning algorithms, like KNN, OC-SVM, iForest, and LOF, is also adapted with two different setups of hyper-parameters to be used in the proposed framework. Results indicate that our framework integrated with Fast-ABOD is able to detect anomalies in traffic data better than other machine learning and state-of-the-art deep learning algorithms with more than 72% and 96% of F1 score and AUC.
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ItemCrowdshipping for sustainable urban logistics: A systematic review of the literature(Elsevier BV, 2023-10)Crowd-Shipping (CS) solutions have been gaining popularity in industry and academia. Despite numerous CS platforms having been introduced in the real world, only a limited number of them have managed to remain viable. Academics have explored many challenges facing CS platforms and recommended appropriate solution measures. While the growing literature sporadically indicates “economic”, “environmental” or even “societal” benefits of CS initiatives, there is a lack of systematic and conclusive understanding of CS initiatives from these essential “sustainability perspectives”. Considering that sustainability and societal impacts of such new and emerging initiatives are key factors in gaining public policy support and potential government investments and involvements, as critical success factors for the uptake, growth and continuity of these initiatives, this paper aims to present a review of this topic in light of sustainability considerations. A content-based framework grounded on the Triple Bottom Line (TBL) approach is adopted in this review and papers are reviewed, classified, synthesised, and analysed to reveal dominant research trends, challenges, potential and gaps. Furthermore, our analysis of the economic and behavioural considerations of CS actors reveals important insights into how various pricing strategies can be adopted to regulate supply and demand for operational continuity. Finally, using an intersectional sustainability approach, future research directions are also recommended to fill the gaps and improve the practical relevance of CS.
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ItemTransferable supervised learning model for public transport service load estimation(Springer Science and Business Media LLC, 2023-07-28)Detailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.
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ItemTemporal pattern mining of urban traffic volume data: a pairwise hybrid clustering method(Taylor and Francis Group, 2023)Multiple pattern analyses of traffic data have been conducted previously; however, it has yet to be explored with an awareness of temporal factors in big real-world traffic data. In this paper, we introduce a hybrid method to measure the intensity of differences among various temporal factors’ data. The proposed method can efficiently process the historical data given temporal factors and provide insightful information about the intensity of variations. After data denoising with basis splines, we reshape the time series into a 2-D latent space using Principal Component Analysis (PCA) according to the type of analysis. Pairwise K-means clustering is then applied after anomaly elimination with DBSCAN to derive Adjusted Rand Index (ARI) matrices. Finally, these matrices are then systematically used to find similar patterns of different temporal perspectives. Multiple analyses are carried out with real data from Melbourne, Australia. Dissimilarities with intensities of up to 80% are detected that are not detectable with general clustering approaches.