Calibration and Validation of A Shared space Model: A Case Study

Shared space is an innovative streetscape design that seeks minimum separation between vehicle traffic and pedestrians. Urban design is moving toward space sharing as a means of increasing the community texture of street surroundings. Its unique features aim to balance priorities and allow cars and pedestrians to coexist harmoniously without the need to dictate behavior. There is, however, a need for a simulation tool to model future shared space schemes and to help judge whether they might represent suitable alternatives to traditional street layouts. This paper builds on the authors’ previously published work in which a shared space microscopic mixed traffic model based on the social force model (SFM) was presented, calibrated, and evaluated with data from the shared space link typology of New Road in Brighton, United Kingdom. Here, the goal is to explore the transferability of the authors’ model to a similar shared space typology and investigate the effect of flow and ratio of traffic modes. Data recorded from the shared space scheme of Exhibition Road, London, were collected and analyzed. The flow and speed of cars and segregation between pedestrians and cars are greater on Exhibition Road than on New Road. The rule-based SFM for shared space modeling is calibrated and validated with the real data. On the basis of the results, it can be concluded that shared space schemes are context dependent and that factors such as the infrastructural design of the environment and the flow and speed of pedestrians and vehicles affect the willingness to share space

Resilience assessment for interdependent urban infrastructure systems using dynamic network flow models

Critical infrastructure systems are becoming increasingly interdependent, which can exacerbate the impacts of disruptive events through cascading failures, hindered asset repairs and network congestion. Current resilience assessment methods fall short of fully capturing such interdependency effects as they tend to model asset reliability and network flows separately and often rely on static flow assignment methods. In this paper, we develop an integrated, dynamic modelling and simulation framework that combines network and asset representations of infrastructure systems and models the optimal response to disruptions using a rolling planning horizon. The framework considers dependencies pertaining to failure propagation, system-of-systems architecture and resources required for operating and repairing assets. Stochastic asset failure is captured by a scenario tree generation algorithm whereas the redistribution of network flows and the optimal deployment of repair resources are modelled using a minimum cost flow approach. A case study on London’s metro and electric power networks shows how the proposed methodology can be used to assess the resilience of city-scale infrastructure systems to a local flooding incident and estimate the value of the resilience loss triangle for different levels of hazard exposure and repair capabilities

ST CrossingPose: a spatial-temporal graph convolutional network for skeleton-based pedestrian crossing intention prediction

Pedestrian crossing intention prediction is crucial for the safety of pedestrians in the context of both autonomous and conventional vehicles and has attracted widespread interest recently. Various methods have been proposed to perform pedestrian crossing intention prediction, among which the skeleton-based methods have been very popular in recent years. However, most existing studies utilize manually designed features to handle skeleton data, limiting the performance of these methods. To solve this issue, we propose to predict pedestrian crossing intention based on spatial-temporal graph convolutional networks using skeleton data (ST CrossingPose). The proposed method can learn both spatial and temporal patterns from skeleton data, thus having a good feature representation ability. Extensive experiments on a public dataset demonstrate that the proposed method achieves very competitive performance in predicting crossing intention while maintaining a fast inference speed. We also analyze the effect of several factors, e.g., size of pedestrians, time to event, and occlusion, on the proposed method

A multi-objective GA-based optimisation for holistic Manufacturing, transportation and Assembly of precast construction

Resource scheduling of construction proposals allows project managers to assess resource requirements, provide costs and analyse potential delays. The Manufacturing, transportation and Assembly (MtA) sectors of precast construction projects are strongly linked, but considered separately during the scheduling phase. However, it is important to evaluate the cost and time impacts of consequential decisions from manufacturing up to assembly. In this paper, a multi-objective Genetic Algorithm-based (GA-based) searching technique is proposed to solve unified MtA resource scheduling problems (which are equivalent to extended Flexible Job Shop Scheduling Problems). To the best of the authors' knowledge, this is the first time that a GA-based optimisation approach is applied to a holistic MtA problem with the aim of minimising time and cost while maximising safety. The model is evaluated and compared to other exact and non-exact models using instances from the literature and scenarios inspired from real precast constructions

Tidal Range Resource of the Patagonian Shelf

With vast potential for renewable energy conversion, the ocean could help reduce our reliance on fossil fuels. Of the various forms of ocean energy, tidal range power is both mature and predictable, dating back to 1966. However, only a few regions of the world are suited to tidal range power. Here, we examine the tidal range potential of the Patagonian shelf – estimated to contain over 100 GW of tidal dissipation. We use a high resolution global tidal atlas (TPXO9) to examine this resource from theoretical and technical perspectives. The theoretical resource is 913 TWh (104 GW) – considerably exceeding neighbouring Argentina’s electricity demand (∼143 TWh in 2021). We find that due to near-resonance with the semidiurnal tides, the resource is concentrated in two regions – Golfo de San Matías, and Bahía Grande to Río Grande. Three sites are chosen for further analysis after considering practical constraints such as water depth and proximity to the electricity grid. Through 0D modelling with tidal range power plant operation we find that the selected sites offer high energy extraction potential, exceeding 40% of the available resource. Further analysis shows how the combination of the sites can reduce the periods of no-generation to under 20%

Monocular visual traffic surveillance: a review

To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined

Calibration and validation of a shared space model: case study

Shared space is an innovative streetscape design that seeks minimum separation between vehicle traffic and pedestrians. Urban design is moving toward space sharing as a means of increasing the community texture of street surroundings. Its unique features aim to balance priorities and allow cars and pedestrians to coexist harmoniously without the need to dictate behavior. There is, however, a need for a simulation tool to model future shared space schemes and to help judge whether they might represent suitable alternatives to traditional street layouts. This paper builds on the authors’ previously published work in which a shared space microscopic mixed traffic model based on the social force model (SFM) was presented, calibrated, and evaluated with data from the shared space link typology of New Road in Brighton, United Kingdom. Here, the goal is to explore the transferability of the authors’ model to a similar shared space typology and investigate the effect of flow and ratio of traffic modes. Data recorded from the shared space scheme of Exhibition Road, London, were collected and analyzed. The flow and speed of cars and segregation between pedestrians and cars are greater on Exhibition Road than on New Road. The rule-based SFM for shared space modeling is calibrated and validated with the real data. On the basis of the results, it can be concluded that shared space schemes are context dependent and that factors such as the infrastructural design of the environment and the flow and speed of pedestrians and vehicles affect the willingness to share space

Approximate optimum curbside utilisation for pick-up and drop-off (PUDO) and parking demands using reinforcement learning

With the uptake of automated transport, especially Pick-Up and Drop-Off (PUDO) operations of Shared Autonomous Vehicles (SAVs), the valet parking of passenger vehicles and delivery vans are envisaged to saturate our future streets. These emerging behaviours would join conventional on-street parking activities in an intensive competition for scarce curb resources. Existing curbside management approaches principally focus on those long-term parking demands, neglecting those short-term PUDO or docking events. Feasible solutions that coordinate diverse parking requests given limited curb space are still absent. We propose a Reinforcement Learning (RL) method to dynamically dispatch parking areas to accommodate a hybrid stream of parking behaviours. A partially-learning Deep Deterministic Policy Gradient (DDPG) algorithm is trained to approximate optimum dispatching strategies. Modelling results reveal satisfying convergence guarantees and robust learning patterns. Namely, the proposed model successfully discriminates parking demands of distinctive sorts and prioritises PUDOs and docking requests. Results also identify that when the demand-supply ratio situates at 2:1 to 4:1, the service rate approximates an optimal (83\%), and curbside occupancy surges to 80%. This work provides a novel intelligent dispatching model for diverse and fine-grained parking demands. Furthermore, it sheds light on deploying distinctive administrative strategies to the curbside in different contexts