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Accurate traffic volume prediction plays a crucial role in urban traffic control by relieving congestion through improved regulation of traffic volume. Network‐level traffic volume prediction and detector failure have rarely been considered in the literature. This paper proposes a framework based on long short‐term memory and the multilayer perceptron that can predict network‐level traffic volumes even with detector failure. A profile model learns the profile of the detector's signature (traffic pattern). Detectors with similar profiles are considered to have similar traffic patterns and are grouped into a cluster. Failed detectors can obtain reference information from similar detectors in the same cluster without additional information. A predictive model is developed for each cluster. The proposed method is validated using Japan Road Traffic Information Center data for three cities. The computational results indicate that the proposed method performs well both on typical days and atypical days (the COVID‐19 lockdown period and the 2021 Tokyo Olympics). Further, it considers detector reliability: the increase in mean absolute error is less than 1 veh/5 min when the probability of detector failure increases to 20%. [ABSTRACT FROM AUTHOR]

Accurate traffic density plays a pivotal role in the Intelligent Transportation Systems (ITS). The current practice to obtain the traffic density is through specialized sensors. However, those sensors are placed in limited locations due to the cost of installation and maintenance. In most metropolitan areas, traffic surveillance cameras are widespread in road networks, and they are the potential data sources for estimating traffic density in the whole city. Unfortunately, such an application is challenging since surveillance cameras are affected by the 4L characteristics: Low frame rate, Low resolution, Lack of annotated data, and Located in complex road environments. To the best of our knowledge, there is a lack of holistic frameworks for estimating traffic density from traffic surveillance camera data with 4 L characteristics. Therefore, we propose a framework for estimating traffic density using uncalibrated traffic surveillance cameras. The proposed framework consists of two major components: camera calibration and vehicle detection. The camera calibration method estimates the actual length between pixels in the images and videos, and the vehicle counts are extracted from the deep-learning-based vehicle detection method. Combining the two components, high-granular traffic density can be estimated. To validate the proposed framework, two case studies were conducted in Hong Kong and Sacramento. The results show that the Mean Absolute Error (MAE) for the estimated traffic density is 9.04 veh/km/lane in Hong Kong and 7.03 veh/km/lane in Sacramento. The research outcomes can provide accurate traffic density without installing additional sensors. [ABSTRACT FROM AUTHOR]

Travel time estimation plays an essential role in the high-granular traffic control and management of urban roads with distinct lane-changing conditions among lanes. However, little attention has been given to the estimation of distributions of travel times among different lanes and different vehicle types in addition to their expected values. This paper proposes a new method for estimating lane-based travel time distributions with consideration of different vehicle types through matching low-resolution vehicle video images taken by conventional traffic surveillance cameras. The vehicle type classification is based on vehicle sizes and deep learning features extracted by densely connected convolutional neural networks, and the vehicle re-identification is conducted through a lane-based bipartite graph matching technique. A case study is carried out on a congested urban road in Hong Kong. Results show that the proposed method performs well in estimating the lane-level travel time distributions by vehicle type which can be very helpful for various lane-based and vehicle type-specific traffic management schemes. [ABSTRACT FROM AUTHOR]

The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles’ lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads. [ABSTRACT FROM AUTHOR]

This article investigates the escort control problem for heterogeneous discrete‐time multi‐agent systems with multiple leaders. We develop a distributed output feedback control law such that the followers are secured within the convex hull spanned by the output of the leaders. First, the followers estimate the target convex hull and the system matrices of the leaders via a distributed observer. We then devise a distributed dynamic output feedback control protocol based on this observer to achieve the escort control by using only neighboring relative output information of leaders. The security of the followers is guaranteed by seeing that the output tracking errors converge to zero exponentially. In the numerical simulations, we discuss the potential deployment of the proposed method to a brand‐new escort control of mixed traffic consist of connected automated vehicles and partially automated vehicles. This vehicular escort control formulation can be regarded as a generalization of the conventional vehicular platoon control problem into both longitudinal and lateral dimensions. The numerical results validate the effectiveness and the computational feasibility of the proposed control protocols. [ABSTRACT FROM AUTHOR]

This paper reports on the findings of pedestrian flow characteristics for different types of walking facilities in Hong Kong. Surveys were conducted during peak hours at indoor and outdoor walkways, signalized crosswalks with and without midblock, signalized and nonsignalized crosswalks leading to light-rail transit stations, and five types of walking facilities in railway stations. The collected data were used to develop speed/flow relationships and to calibrate the travel time function for each type of pedestrian facility. Pedestrian flow characteristics on various walking facilities are also compared. The collected data and the established relationships could be used as a basis for the development of pedestrian simulation models for Hong Kong and/or other Asian cities with similar pedestrian characteristics.

Summary: This paper presents an overview of the structural performance monitoring system for a butterfly‐shaped arch footbridge located at The Hong Kong Polytechnic University. The system is the product of a multidisciplinary academic collaboration of the pooling expertise of four academic departments, namely, structural engineering, traffic engineering, surveying, and optical communication engineering. This system consists of 88 sensors of 13 types, including an advanced Brillouin optical time‐domain analysis technology, an optical fiber Bragg grating technology, a global navigation satellite system (GNSS), and high‐performance video cameras. The paper summarizes the modular design, subsystem framework, and advanced features of the monitoring system. Preliminary monitoring results, such as high spatial resolution distributed temperature/strain measurement, global heat‐transfer analysis, pedestrian traffic analysis, dynamic characteristics, and GNSS measurements, are introduced. A fire accident is employed to demonstrate the function and value of the monitoring system and provided a rare opportunity to detect damages in a real structure. The monitoring system not only monitors the performance of the bridge in real‐time but also provides a multifunctional platform, including an Internet‐based software for research and teaching and a touchscreen visualization platform for public education. [ABSTRACT FROM AUTHOR]

Walking is a mode of transport that offers many benefits. This study aims to provide insights on the emotions associated with different types of walking episodes – namely, utilitarian walking episodes that are undertaken with the purpose of fulfilling an activity at a destination and recreational walking episodes that are undertaken with no specific purpose/destination. A knowledge of the emotions associated with different types of walking episodes can help steer policies and investments in a way that would engender greater levels of walking. The paper utilizes the well-being module of the American Time Use Survey (ATUS) data set to model subjective ratings on five different emotions associated with walking, employing a total of 1583 walk episodes. The five emotions include happiness, meaningfulness, tiredness, stress, and painfulness. A multivariate ordered probit model is estimated to account for unobserved attributes that may simultaneously affect multiple emotions. Model estimation results show that a number of socio-demographic variables and walking episode attributes affect how people feel about recreational and utilitarian walking episodes. Overall, it is found that utilitarian walking episodes offer a lower level of positive emotions than recreational episodes – presumably because recreational episodes are undertaken in more pleasant environments under more relaxing conditions. The results suggest that investments in recreational walking infrastructure and green spaces may yield richer dividends in terms of engendering higher levels of walking. Targeting specific socio-demographic groups for awareness campaigns and having strategic parking policies based on the day of the week are some of the other important implications. [ABSTRACT FROM AUTHOR]

The problem of finding the K shortest paths (KSPs) between a pair of nodes in a road network is an important network optimization problem with broad applications. Yen's algorithm is a classical algorithm for exactly solving the KSP problem. However, it requires numerous shortest path searches, which can be computationally intensive for real large networks. This study proposes a fast algorithm by introducing a generalized spur path reuse technique. Using this technique, shortest paths calculated during the KSP finding process are stored. Accordingly, many shortest path searches can be avoided by reusing these stored paths. The results of computational experiments on several large‐scale road networks show that the introduced generalized spur path reuse technique can avoid more than 98% of shortest path searches in the KSP finding process. The proposed algorithm speeds up Yen's algorithm by up to 98.7 times in experimental networks. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative method for bus passenger waiting time estimation using passive Wi-Fi data. With the underlying mechanism for Wi-Fi communication, the presence of Wi-Fi enabled devices in a particular area can be passively discoverable. The mobile devices carried by bus passengers can be exploited to estimate passenger waiting time at a bus stop without the passengers’ direct participation. Passenger waiting time estimation using such opportunistic data is challenging due to the particular characteristics of the Wi-Fi data collected from bus stop environments. This paper proposes a methodology to handle massive noise in Wi-Fi data and identify the potential Wi-Fi records which are derived from passengers’ devices. The filtered data can then be used to estimate passenger waiting time. The Wi-Fi data collected from a bus stop in Hong Kong are used as a case study for evaluating the proposed system. The practicality is investigated in terms of estimation accuracy and insightful analysis. [ABSTRACT FROM AUTHOR]

Limited-stop bus services are a highly efficient way to release more potential of the public transit system to meet travel demand, especially under constraints on vehicle fleet size and transportation infrastructure. This work first proposes a visualized fare table for the design of limited-stop bus services along a public transit corridor, along which many lines of public transit carry a heavy load of demand back and forth every working day. Based on this proposed fare table, a set of fare strategies and desired aims of fare policy, a differentiated fare structure is established to improve social equity and increase revenue. The nature of the structure can help travellers understand how to be charged between their origins and destinations (e.g. flat, time-based, stop-based or quality-based pricing) and then plan their trips efficiently. Secondly, a model is formulated to minimize the total social cost in designing a fixed demand limited-stop bus service system with a differentiated fare structure. Thirdly, numerical results are carried out with sensitivity analysis within three scenarios of differentiated fare structures. It is found that a differentiated fare structure has a great effect on passenger path choice behaviour and resulting optimal design of bus services. An attractive feature of this differentiated fare structure is that it could not only enhance the operator’s revenue and social equity but also reduce passenger transfers and social cost. [ABSTRACT FROM AUTHOR]

Road transport networks that exist within Eastern Asia are experiencing ever-increasing levels of demand due to rapid population growth within the region. This is especially so for interregional travel because of a continuous dispersion of the population as it expands. Within Hong Kong, new transport infrastructure is required to cater to the demands emerging from growth and decentralization of the population distribution. Initiatives such as park-and-ride schemes can assist transport networks to operate more efficiently and are worthy of assessment as a demand management technique for inclusion in the future transit system. As a result of a comprehensive survey conducted on a trial park-and-ride facility in Hong Kong, lessons have been learned for the implementation of future schemes within Hong Kong and Eastern Asia. This paper provides a description of the data assembled as a result of the survey, and it identifies reasons and factors contributing to the success of the park-and-ride facility in Hong Kong.

Spatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches. [ABSTRACT FROM AUTHOR]

Over the past decades, many activity-based travel behaviour models have been proposed based on individuals’ independent decision making. The modelling of individuals’ joint activity/travel choices, however, has received less attention. In reality, both independent and joint activities/travels form individual’s normal daily activity-travel patterns. Travel surveys have indicated that joint activity/travel constitutes an important part in individuals’ daily activity-travel patterns. On this basis, explicit modelling of joint activity/travel choices is an essential component for long-term transport planning. In this study, an activity-based network equilibrium model is proposed for scheduling two-individual joint activity-travel patterns (JATPs) in congested multi-modal transit networks. The proposed model can be used to comprehensively investigate individuals’ activity choices (e.g. activity start time and duration, activity sequence) and travel choices (e.g. departure time, route and mode) in multi-modal transit networks, including both independent ones and joint ones. The time-dependent JATP choice problem is converted into an equivalent static user equilibrium model by constructing a joint-activity-time-space (JATS) super-network platform. Joint travel benefit is modelled by incorporating a commonality factor in the JATP utility. A solution algorithm without prior JATP enumeration is proposed to solve the JATP scheduling problem on the JATS super-network. Numerical results show that individuals’ independent and joint activity/travel choices can be simultaneously investigated by the proposed model. The impacts of joint travel benefit on individuals’ independent and joint activity-travel choices are explicitly investigated. [ABSTRACT FROM AUTHOR]

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. [ABSTRACT FROM AUTHOR]

Eco-routing aims to provide fuel-efficient paths to help travellers make route choice decisions in the road network. Most eco-routing algorithms build on a deterministic assumption about link fuel consumption. However, link fuel consumption in real road networks is highly stochastic caused by travel speed variations. This study investigates the eco-routing problem by explicitly considering uncertainties of travel time and fuel consumption. A stochastic fuel consumption formula to estimate fuel consumption distributions caused by travel speed variations is proposed. A bi-objective reliable eco-routing model is developed by minimizing the travel time budget and fuel consumption budget simultaneously while satisfying given constraints on travel time reliability and fuel consumption reliability. An efficient reliable path ranking algorithm is developed to solve the formulated model exactly. A comprehensive case study using real data from Hong Kong is presented to demonstrate the applicability of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Travel time uncertainty has significant impacts on individual activity-travel scheduling, but at present these impacts have not been considered in most accessibility studies. In this paper, an accessibility evaluation framework is proposed for urban areas with uncertain travel times. A reliable space-time service region (RSTR) model is introduced to represent the space-time service region of a facility under travel time uncertainty. Based on the RSTR model, four reliable place-based accessibility measures are proposed to evaluate accessibility to urban services by incorporating the effects of travel time reliability. To demonstrate the applicability of the proposed framework, a case study using large-scale taxi tracking data is carried out. The results of the case study indicate that the proposed accessibility measures can evaluate large-scale place-based accessibility well in urban areas with uncertain travel times. Conventional place-based accessibility indicators ignoring travel time reliability can significantly overestimate the accessibility to urban services. [ABSTRACT FROM AUTHOR]

This article proposes a novel urban system equilibrium model for design of a tradable location tax credit scheme to balance traffic congestion and environmental externalities. In the proposed model, the interaction between the location tax credit scheme and households' residential location choices is explicitly considered. It is assumed that the authority initially allocates the credits to all households in a uniform way, and the households pay a certain amount of credits for housing consumptions, relying on the traffic congestion and environmental externalities that they cause. The credits are traded among households through a free market, and the tax revenue due to credit trading is considered as a part of household's income to be redistributed through household's income budget constraint. For a given credit scheme, households' residential location choices and housing market structure in terms of housing prices and space can be endogenously determined by the urban system equilibrium and the price per unit of credit is governed by the credit market equilibrium. A social welfare maximization model is presented to determine the total amount of credits issued. The results show that implementation of the tradable location tax credit scheme can rationalize the urban residential density and promote the efficiency of the urban system in terms of social welfare. [ABSTRACT FROM AUTHOR]

Travel times are generally stochastic and spatially correlated in congested road networks. However, very few existing route guidance systems (RGS) can provide reliable guidance services to aid travellers planning their trips with taking account explicitly travel time reliability constraint. This study aims to develop such a RGS with particular consideration of travellers' concern on travel time reliability in congested road networks with uncertainty. In this study, the spatially dependent reliable shortest path problem (SD-RSPP) is formulated as a multi-criteria shortest path-finding problem in road networks with correlated link travel times. Three effective dominance conditions are established for links with different levels of travel time correlations. An efficient algorithm is proposed to solve SD-RSPP by adaptively using three established dominance conditions. The complexities of road networks in reality are also explicitly considered. To demonstrate the applicability of proposed algorithm, a comprehensive case study is carried out in Hong Kong. The results of case study show that the proposed solution algorithm is robust to take account of travellers' multiple routing criteria. Computational results demonstrate that the proposed solution algorithm can determine the reliable shortest path on real-time basis for large-scale road networks. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, two-tier mathematical models were developed to simulate the microscopic pedestrian decision-making process of route choice at signalized crosswalks. In the first tier, a discrete choice model was proposed to predict the choices of walking direction. In the second tier, an exponential model was calibrated to determine the step size in the chosen direction. First, a utility function was defined in the first-tier model to describe the change of utility in response to deviation from a pedestrian's target direction and the conflicting effects of neighboring pedestrians. A mixed logit model was adopted to estimate the effects of the explanatory variables on the pedestrians' decisions. Compared with the standard multinomial logit model, it was shown that the mixed logit model could accommodate the heterogeneity. The repeated observations for each pedestrian were grouped as panel data to ensure that the parameters remained constant for individual pedestrians but varied among the pedestrians. The mixed logit model with panel data was found to effectively address inter-pedestrian heterogeneity and resulted in a better fit than the standard multinomial logit model. Second, an exponential model in the second tier was proposed to further determine the step size of individual pedestrians in the chosen direction; it indicates the change in walking speed in response to the presence of other pedestrians. Finally, validation was conducted on an independent set of observation data in Hong Kong. The pedestrians' routes and destinations were predicted with the two-tier models. Compared with the tracked trajectories, the average error between the predicted destinations and the observed destinations was within an acceptable margin. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The heteroscedasticity refers to a collection of random variables with a subpopulation that have different dispersions from others. The variable dispersion could be quantified by measures of statistical dispersion such as standard deviation or coefficient of standard deviation. This study aims to model the effects of rainfall intensity on the heteroscedastic traffic speed dispersion on urban roads. The traffic and rainfall intensity data were collected by a selected video traffic detector and its nearest rainfall station in Hong Kong, respectively. The coefficient of variation of speed (CVS) was employed to measure the vehicular traffic speed dispersion. The analysis shows that the empirical values of CVS typically range from 0.05 to 0.2 at different traffic densities and rainfall intensities, and the exponential function provides a good fit to traffic speed data under both dry and rain conditions. A generalized function of CVS with the effects of rainfall intensity is proposed, calibrated, and validated with different sets of empirical data. The calibration and validation results show that the proposed generalized function of CVS fits well with the empirical data. The empirical findings and the generalized function of CVS proposed in this study may benefit for assessing and modeling the level-of-service performance of urban roads in Pacific Rim cities similar to Hong Kong with relatively high annual rainfall intensity. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the problem on traffic data fusion for travel times estimation, the prediction of work zone traffic capacity for traffic management, and the algorithm for traffic assignment problem.

Turn restrictions, such as 'no left turn' or 'no U-turn', are commonly encountered in real road networks. These turn restrictions must be explicitly considered in the shortest path problem and ignoring them may lead to infeasible paths. In the present study, a hybrid link-node Dijkstra's (HLND) algorithm is proposed to exactly solve the shortest path problem in road networks with turn restrictions. A new hybrid link-node labelling approach is devised by using a link-based labelling strategy at restricted nodes with turn restrictions, and a node-based labelling strategy at unrestricted nodes without turn restrictions. Computational results for several real road networks show that the proposed HLND algorithm obtains the same optimal results as the link-based Dijkstra's algorithm, while having a similar computational performance to the classical node-based Dijkstra's algorithm. [ABSTRACT FROM AUTHOR]