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

Strategic maritime container transport design in oligopolistic markets

AbstractThis paper considers the maritime container assignment problem in a market setting with two competing firms. Given a series of known, exogenous demands for service between pairs of ports, each company is free to design a liner service network serving a subset of the ports and demand, subject to the size of their fleets and the potential for profit. The model is designed as a three-stage complete information game: in the first stage, the firms simultaneously invest in their fleet; in the second stage, they individually design their networks and solve the route assignment problem with respect to the transport demand they expect to serve, given the fleet determined in the first stage; in the final stage, the firms compete in terms of freight rates on each origin-destination movement. The game is solved by backward induction. Numerical solutions are provided to characterize the equilibria of the game

PRINCIPAL COMPONENT ANALYSIS OF NEURAL AND KINEMATIC PARAMETERS OF FORWARD AND BACKWARD WALKING ACROSS DIFFERENT INCLINES

The purpose of this study was to identify whether the motor pattern of forward walking (FW) and backward walking (BW) affects the neural control and kinematics of lower limbs. A 21-camera 3D motion analysis system was used for the examination of locomotion. The activation of seven muscles of the right leg was recorded. The motion analysis was performed during FW and BW on a treadmill with the subjects (n=15) walking at four different inclines. The primary analysis of the complexity of variability of the kinematics and neural data was assessed using Principal Component Analysis (PCA). The complexity of gait pattern during FW appears more varied than during BW across all inclines. The associated muscles with each component were different during FW and BW

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

Effect of three-dimensional mixing conditions on water treatment reaction process

The performance of water disinfection facilities traditionally relies on Hydraulic Efficiency Indicators (HEIs), extracted from experimentally derived Residence Time Distribution (RTD) curves. This approach has often been undertaken numerically through computational fluid dynamics (CFD) models, which can be calibrated to predict accurately RTDs, enabling the assessment of disinfection facilities prior to the construction of disinfection tanks. However, a significant drawback of the conventional efficiency methodology prescribed for disinfection tanks is associated with the respective indicators, as they are predominantly linked to the internal flow characteristics developed in the reactor, rather than the disinfection chemistry which should be optimized. In this study three-dimensional numerical models were refined to simulate the processes of chlorine decay, pathogen inactivation and the by-product formation in disinfection contact tanks (CTs). The main objective of this study was to examine the effect of three-dimensional mixing on the reaction processes which were modelled through finite-rate kinetic models. Comparisons have been made between pathogen inactivation and disinfection by-product accumulation results produced by a RANS approach against the findings of a Segregated Flow Analysis (SFA) of conservative tracer transport. CFD Results confirm that three-dimensional mixing does have an effect on the reaction processes, which, however, is not apparent through the SFA approach