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Resilience concepts in integrated urban transport refer to the performance of dealing with external shock and the ability to continue to provide transportation services of all modes. A robust transportation resilience is a goal in pursuing transportation sustainability. Under this specified context, while before the perturbations, robustness refers to the degree of the system’s capability of functioning according to its design specifications on integrated modes and routes, redundancy is the degree of duplication of traffic routes and alternative modes to maintain persistency of service in case of perturbations. While after the perturbations, resourcefulness refers to the capacity to identify operational problems in the system, prioritize interventions and mobilize necessary material/ human resources to recover all the routes and modes, rapidity is the speed of complete recovery of all modes and traffic routes in the urban area. These “4R” are the most critical components of urban integrated resilience.

The trends of transportation resilience's connotation, metrics and strategies are summarized from the literature. A framework is introduced on both qualitative characteristics and quantitative metrics of transportation resilience. Using both model-based and mode-free methodologies that measure resilience in attributes, topology and system performance provides a benchmark for evaluating the mechanism of resilience changes during the perturbation. Correspondingly, different pre-perturbation and post-perturbation strategies for enhancing resilience under multi-mode scenarios are reviewed and summarized.

Cyber-physic transportation system (CPS) is a more targeted solution to resilience issues in transportation. A well-designed CPS can be applied to improve transport resilience facing different perturbations. The CPS ensures the independence and integrity of every child element within each functional zone while reacting rapidly.

This paper provides a more comprehensive understanding of transportation resilience in terms of integrated urban transport. The fundamental characteristics and strategies for resilience are summarized and elaborated. As little research has shed light on the resilience concepts in integrated urban transport, the findings from this paper point out the development trend of a resilient transportation system for digital and data-driven management.

Due to the conflicts between left turn traffic and opposite straight-going traffic in urban traffic network, some of the traffic lanes cannot be used to discharge vehicles during its green phases and the intersection capacity can be greatly reduced. This study/paper aims to reduce the effect of conflicts and increase its capacity through the reasonable pre-signal phase time with the exchangeable lanes.

This paper took into consideration various influence factors to intersection capacity and formulated the capacity optimization model based on 0-1 mixed-integer programming model. This model is efficiently solved by standard branch-and-bound algorithms.

The authors took an intersection as an example and solved the optimal signal timing and entrance lane capacity via this model. Then, simulations were carried out to verify the effect of the exchangeable lanes strategy of this intersection through the simulation software VISSIM and take the traffic volume and delay as outputs, which indicated that this model has better performance.

The front-end control strategy can not only exploit the full potential of the intersection but also significantly improve the operational efficiency of the intersection. It plays a positive role in improving urban intersection congestion.

Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management.

Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index.

This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index.

Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework.

The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level.

MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only.

This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

This study aims to propose a speed guidance model of the CV environment to alleviate traffic congestion at intersections and improve traffic efficiency. By introducing the theory of moving block section for high-speed train control, a speed guidance model based on the quasi-moving block speed guidance (QMBSG) is proposed to direct platoon including human-driven vehicles and connected vehicles (CV) through the intersection coordinately.

In this model, the green time of the intersection is divided into multiple block intervals according to the minimal safety headway. Connected vehicles can pass through the intersection by following the block interval using the QMBSG model. The block interval is assigned dynamically according to the traveling relation of HV and CV, when entering the communication range of the intersection. To validate the comprehensive guidance effect of the proposed model, a general evaluation function (GEF) is established. Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement.

Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Also, compared to the single intersection speed guidance model, the GEF value of the QMBSG model improves over 17.1%. To further explore the guidance effect, the impact of sensitivity factors of the CVs’ environment, such as intersection environment, communication range and penetration rate (PR) is analyzed. When the PR reaches 75.0%, the GEF value will change suddenly and the model guidance effect will be significantly improved. This paper also analyzes the impact of the length of block interval under different PR and traffic demands. It is found that the proposed model has a better guidance effect when the length of the block section is 2 s, which facilitates traffic congestion alleviation of the intersection in practice.

Based on the aforementioned discussion, the contributions of this paper are three-fold. Based on the traveling information of HV/CV and the signal phase and timing plans, the QMBSG model is proposed to direct platoon consisting of HV and CV through the intersection coordinately, by following the block interval assigned dynamically. Considering comprehensively the indexes of mobility, safety and environment, a GEF is provided to evaluate the guidance effect of vehicles through the intersection. Sensitivity analysis is carried out on the QMBSG model. The key communication and traffic parameters of the CV environment are analyzed, such as path attenuation, PR, etc. Finally, the effect of the length of block interval is explored.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

Recent advancements in information and communication technologies have led to the rapid growth of electronic commerce market. In the United States, e-commerce retail sales for 2002 reached $45.6 billion, indicating an increase of 26.9% from 2001 while total retail sales increased 3.1% during the same period. Although e-commerce sales account for only 1.4% of total sales in this country, forecasts show that the magnitude of digital economy will continue to expand. The logistical requirements of e-commerce goods that extend to each customer's address stimulate greater complexity in traditional supply chain management, potentially causing higher costs for freight supply chain participants. To harness the economic potential of e-commerce, it is important to encourage the development of a freight transportation system that will support its steady growth, while avoiding the possible negative effects from the changes in freight transportation. Due to the intrinsic nature of e-commerce goods, advances in home delivery have the potential to promote the growth of e-commerce as well as to create sustainable urban freight transportation systems. Based on the case study of the United States, this paper presents an in-depth discussion of the key challenges arising in home delivery operations, and proposes potential solution strategies that will lead to more efficient and reliable home delivery systems.

Air quality and its assessment in urban areas has become a necessity. This is attributed to the increasing air pollution in urban landscape from anthropogenic activities necessary for economic growth and development. This study investigates air quality and potential health risk posed from nitrogen dioxide (NO₂) to the residents of low town of Mohammedia city, Morocco.

The NO₂ concentration was measured on an hourly basis for the winter season of the year 2014, 2015 and 2016. The air quality was assessed in terms of Air Quality Index (AQI). Noncarcinogenic risk assessment was done to evaluate possible health risk to the inhabitant of low town from NO₂ exposure.

The maximum concentration reached 85–96 µg/m³ (at 6 p.m., 2014), 96–104 µg/m³ (7–9 p.m., 2015) and 102–117 (8–11 p.m., 2016). The AQI during maximum NO₂ levels (peak hours) ranged between 0–50 µg/m³ (good) to 51–100 µg/m³ (unhealthy for sensitive group). The risk quotient (RQ) was calculated for average daily intake and average hourly intake of NO₂. RQ was found to be less than 1 (no potential health risk, lifetime and hourly) for all three years. However, increase in RQ value from 0.84 (2014) to 0.98 (2016) indicates increase in potential health risk. Hence, policy and measures should be adopted to reduce the potential health risk.

This study is very first of its kind for the area and hence can serve as reference study for future works. Further studies are required to assess air pollution in other seasons (summer, spring, autumn), impact of climatic condition and parameters on air quality. Also, for direct impact assessment number of cases attributed to air pollution needs to be investigated.

The construction industry is reluctant to utilize construction logistics centres (CLC). To understand why, the purpose of the study is to increase the understanding of drivers and challenges of CLC utilization.

Adopting an activity-based cost methodology, nine construction projects' CLC utilization in a Swedish urban development project is analyzed for a time period of three years (2013–2016).

There is a difference between drivers and challenges for implementing and utilizing CLCs, which are often implemented to reduce third-party disturbances. The drivers to utilize a CLC, however, stem from achieving efficient logistics. The authors identify two different utilization strategies; the CLC is used either for just-in-time (JIT) deliveries or as a consolidation point.

The study shows that construction logistics is to some extent repeating some mistakes of its big brother, city logistics. The study hypothesizes that there are differences between CLC implementation and utilization drivers and challenges that are often overlooked, which can explain the lack of CLC utilization.

The study shows that it is crucial for construction projects to develop and formulate a logistics strategy for how to utilize the setup, i.e. deciding whether to use the setup as a consolidation point leading to fewer deliveries and more materials on-site, or as JIT, leading to more deliveries but fewer materials on-site.

CLCs can reduce environmental impact and third-party disturbances. However, to make this come true, acceptance among the users is needed. The study provides understanding of drivers and barriers for CLC implementation and utilization that can increase acceptance among users (i.e. construction projects).

The current study is one of the first studies to analyze CLC utilization.