•PPP is an effective pathway for large-scaled infrastructure construction and achieving SDGs in New Zealand.•For a 15-year concession period under PPP, the private investor can expect a 12.5% ...return.•The government can charge roadway users a toll of 37 cents/kWh while contributing 9.46% towards the initial investment.•The dynamic charging infrastructure of EVs using DIPT technology is a better investment opportunity with high reliability.•With the DIPT system, EVs could achieve a significant reduction in CO2 emissions compared to ICEs.
Electric vehicles (EVs) are contributing to a decarbonisation of conventional Internal Combustion Engines (ICEs) in the transport sector. With recent technological advancements in Dynamic Inductive Power Transfer (DIPT) system, EVs can be energised wirelessly by embedding a roadway charging network while travelling in-motion. However, the provision of a viable DIPT system still remains challenging, given the large-scaled investment required and some potential risks involved. This study assesses the economic viability of a DIPT system for EVs through public–private partnership (PPP), by employing a net present value (NPV) framework, to determine the optimal PPP ratio. The PPP model could be considered an effective pathway for leveraging capital, alleviating uncertainties associated with construction and operation, and achieving a nation's Sustainable Development Goals (SDGs). New Zealand is used as a real-world case study. Our results indicate that, for a 15-year concession period under PPP where the private investor is expecting a 12.5% return, the government can contribute 9.46% towards the initial investment and charging roadway users a toll of 37 cents/kWh. By implementing the DIPT system, EVs could achieve a significant reduction in carbon dioxide (CO2) emissions compared to ICEs. The robustness of the model is validated through Monto Carlo sensitivity analysis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Short-term traffic prediction is a key component of Intelligent Transportation Systems. It uses historical data to construct models for reliably predicting traffic state at specific locations in road ...networks in the near future. Despite being a mature field, short-term traffic prediction still poses some open problems related to the choice of optimal data resolution, prediction of nonrecurring congestion, and the modelling of relevant spatiotemporal dependencies. As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. We also explore different feature selection methods to identify and better understand the spatiotemporal attributes that most influence the reliability of these models. Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models. The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%. Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection.
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CEKLJ, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK
Variable speed limit (VSL) has proven to be an efficient motorway control measure to mitigate traffic congestion at motorway bottlenecks. VSL can reduce the effect of shockwave on traffic conditions ...through smoothing the transition between the congested downstream and upstream traffic flows. In this paper, a particle swarm optimization (PSO) based VSL method is proposed to minimize the probability of flow breakdown at bottleneck sections and ensure a maximum utilization of the current motorway infrastructure. The results show that the motorway system equipped with PSO based VSL outperformed the one with rule based VSL.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
High cost and relatively low reliability of stationary sensors hinder the wide spread of advanced motorway traffic control measures. In this research, we propose a vehicle trajectory data based ...variable speed limit (VSL) controller to improve mobility and environmental performance of motorways. First, a model based estimator is designed to estimate traffic states using data directly derived from probe vehicle with spacing measurement equipment (PVSMEs). Extended kalman filter (EKF) and METANET model are employed as the data assimilation tool and the process model for the estimator, respectively. Next, we incorporate the estimator with a model predictive control (MPC) to realize optimal VSL control. Finally, a 3.2km stretch in Auckland, New Zealand is selected and simulated to evaluate the proposed VSL under various PVSME penetration rates and traffic scenarios. The simulation results reveal that the PVSME-based VSL controller offers an effective solution to improve mobility and environmental performance of motorways. With an increase in PVSME penetration rates, the mobility and environmental benefits of the PVSME-based VSL increase.
Drunk driving is among the main causes of urban road traffic accidents. Currently, contact-type and non-real-time random inspection are methods used to verify whether drivers are drunk driving. ...However, these techniques cannot meet the actual demand of drunk driving testing. This study considers the following traffic parameters as inputs: speed-up, even-speed, and sharp-turn road segments; vehicle speed; acceleration and accelerator pedal position; and engine speed. Thereafter, this study adopts the support vector machine model to identify drivers’ driving behaviors to determine whether they are drunk driving, as well as the particle swarm optimization algorithm to optimize the model, thereby improving training speed. Results show that the support vector machine model based on the particle swarm optimization algorithm can immediately and accurately determine the drunk driving state of drivers, provide theoretical support to non-contact drunk driving test, and realize the foundation of safety driving assistance system toward the adoption of the corresponding measures. Therefore, this study has positive significance in improving traffic safety.
Ramp metering (RM) has been widely applied due to its effectiveness in improving motorway traffic conditions by limiting inflow from on-ramps. A great deal of experimental and simulation-based ...studies have proven system-wide benefits of motorways from RM. Benefits attributed to RM in the literature include reducing travel times, increasing motorway throughputs and decreasing fuel consumption and emissions. However, RM benefits might be costing more some motorway users, e.g. some on-ramp users might be experiencing longer delay than others, which leads to an unfair allocation of RM benefits. This paper presents a coordinated ramp metering strategy, which is aimed at reducing the inequity among motorway users using different on-ramps and investigates trade-offs between efficiency and equity for the proposed strategy. Total travel time is used to measure the efficiency while Gini coefficient is used to measure equity. A combined index is proposed incorporating the two measures to serve as an objective function to solve the bi-objective control design problem. The performance of the proposed strategy is verified by comparing it to a well-established coordinated ramp metering strategy HERO using micro-simulationsoftware AIMSUN. Simulation results revealed that the equity of the motorway system can be improved significantly by using the proposed strategy without compromising much on the efficiency of the system.
Shared Autonomous Vehicles (SAVs), serving as an alternative to private cars, are emerging as a pivotal solution to severe traffic problems. Leveraging Dynamic Wireless Charging (DWC) technology, ...SAVs can potentially operate continuously meet passenger demands, presenting a complex challenge known as the SAV routing and dynamic wireless charging problem (SVRCP). However, traditional heuristic algorithms, which typically reliant on extensive experiential rule settings, fall short in addressing the multiple objectives and complex constraints of SVRCP. Therefore, we propose a Deep Slack Induction by String Removals-based Reinforcement Learning (DSRL) framework, specifically designed to optimise cost-efficiency and energy stabilisation in SAVs operating with DWC conditions. First, a traffic-vehicle cooperation model, grounded in real-world road networks, is constructed to facilitate interactions for the DSRL's encoder. We then integrate the Slack Induction by String Removals algorithm to adaptively optimise the DSRL parameters, enhancing the decoder's ability to find reliable solutions. Experiments show that DSRL outperforms heuristic methods in optimising route cost and stabilising the State-of-Charge (SOC) of SAVs, while achieving a more centralised <inline-formula> <tex-math notation="LaTeX">\Delta</tex-math> </inline-formula>SOC distribution, ensuring the continuous operational capability of the SAVs. The results across instance sizes and DWC power demonstrate DSRL's robust generalisation capabilities and efficient training capabilities facilitated by transfer learning. Additionally, sensitivity analysis from the perspectives of depot location, passenger distribution, and power levels offers insights for DWC road layout.
•Evacuation behaviour of individual under an imminent threat of volcanic eruption.•Logistic regression approach with odds ratios for analysing influential factors.•Risk awareness and choosing car to ...evacuate have most positive impact on evacuation.•Higher annual household income and household with children have positive influence.•Ethnicity, destination choice and length of residency impacts evacuation decision.
With natural hazard events increasing globally, it is important to establish an effective evacuation procedure to mitigate their impacts. This paper investigates factors contributing to individuals’ evacuation decision-making under an imminent threat of volcanic eruption based on the data collected from a stated preference survey conducted in Auckland, New Zealand. Several factors are analysed using a logistic regression approach, including socio-demographic factors and factors related to risk, awareness, preparedness, evacuation warning and order, evacuation route choice, evacuation mode choice and evacuation destination choice. The results revealed that some of these factors are influential for individuals’ evacuation decision-making, including ethnicity, choice of destination, mode of transport, length of residency, risk awareness, annual household income and household with children. These findings will be useful for planners and policymakers in managing risks and planning to improve the safety of the vulnerable community by identifying appropriate evacuation strategies and reducing risk-increasing evacuation behaviour.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The traditional method of traffic signal design is based on the assumption of a constant queue discharge rate, also termed the saturation flow rate. However, a number of field studies conducted in ...Taiwan and the USA contradict this assumption because they reported a marginally increasing trend observed for the queue discharge rate toward the back of the queue. This paper reports on a field study conducted in Auckland, New Zealand at six signalized intersections. The field observations confirm the findings of previous research, namely that the queue discharge rate increases toward the back of the queue. Two empirical models are proposed to reflect the observed queue discharge behavior. One of them is implemented to calculate the capacity and signal timings. The proposed model shows the potential to overcome the shortcomings of the traditional method in practical applications.
•Preparedness intention to evacuate predict evacuation intention under volcanic hazards.•Attitude is the most influential positive predictor of preparedness intention.•Risk perception positively ...influences preparedness intention to evacuate.•Individuals who seek information are more likely to wait for an evacuation order.
Understanding evacuation decisions of individuals is imperative to establish policies and procedures during emergencies and for long-term societal reconstruction. Five socio-psychological factors contributing to individuals’ evacuation intention under natural hazard conditions are identified from a literature review. These include attitude towards advice given by the authorities, risk perception, social norm, perceived behavioural control and preparedness intention. A conceptual framework is proposed based on the socio-psychological approach to model and analyse the evacuation intention. An online behavioural expectations questionnaire survey is conducted in Auckland, New Zealand, to investigate the evacuation decision-making behaviour of Aucklanders under a hypothetical volcanic eruption scenario. The proposed framework is implemented using the structural equation modelling approach. The results support the proposed model and provide some insights into interrelationships between the predictive factors. Psychological factors, including attitude towards advice given by the authorities, preparedness intention and risk perception, have a significant positive impact on individuals’ evacuation intentions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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