Social Equity Impacts of Congestion Management Strategies

This white paper examines the social equity impacts of various congestion management strategies. The paper includes a comprehensive list of 30 congestion management strategies and a discussion of equity implications related to each strategy. The authors analyze existing literature and incorporate findings from 12 expert interviews from academic, non-governmental organization (NGO), public, and private sector respondents to strengthen results and fill gaps in understanding. The literature review applies the Spatial – Temporal – Economic – Physiological – Social (STEPS) Equity Framework (Shaheen et al., 2017) to identify impacts and classify whether social equity barriers are reduced, exacerbated, or both by a particular congestion mitigation measure. The congestion management strategies discussed are grouped into six main categories, including: 1) pricing, 2) parking and curb policies, 3) operational strategies, 4) infrastructure changes, 5) transportation services and strategies, and 6) conventional taxation. The findings show that the social equity impacts of certain congestion management strategies are not well understood, at present, and further empirical research is needed. Congestion mitigation measures have the potential to affect travel costs, commute times, housing, and accessibility in ways that are distinctly positive or negative for different populations. For these reasons, social equity implications of congestion management strategies should be understood and mitigated for in planning and implementation of these strategies

Using digitalisation for data-driven freight curbside management. A perspective from urban transport planning

Given trends in urbanisation, e-commerce, active mobility and modal shifts, streets have sprung up as scenes of conflict where competing demands for curbside space have increased. Because public space is limited, urban transport planners are called to solve public space conflicts by defining how much space is allocated to specific users as a means to achieve sustainable cities. In the allocation of curbside space, freight parking operations are sometimes overlooked compared to other curbside uses such as private vehicles parking. However, limited space for freight deliveries generates negative impacts on urban traffic (e.g. due to double parking), as well as on emissions and companies’ efficiency (e.g. due to the need to cruise for parking). This thesis aims to contribute to current understandings of the need for and uses of data to inform curbside management decision-making for freight parking from the perspective of urban transport planning. To that end, a case study was conducted to collect and analyse data about freight curbside operations using quantitative and qualitative methods, and a cross-sectional research design facilitated the exploration of the impacts of curbside interventions on cities’ sustainability worldwide

Surrogate-based Real-time Curbside Management for Ride-hailing and Delivery Operations

The present work investigates surrogate model-based optimization for real-time curbside traffic management operations. An optimization problem is formulated to minimize the congestion on roadway segments caused by vehicles stopping on the segment (e.g., ride-hailing or delivery operations) and implemented in a model predictive control framework. A hybrid simulation approach where main traffic flows interact with individually modeled stopping vehicles is adopted. Due to its non-linearity, the optimization problem is coupled with a meta-heuristic. However, because simulations are time expensive and hence unsuitable for real-time control, a trained surrogate model that takes the decision variables as inputs and approximates the objective function is employed to replace the simulation within the meta-heuristic algorithm. Several modeling techniques (i.e., linear regression, polynomial regression, neural network, radial basis network, regression tree ensemble, and Gaussian process regression) are compared based on their accuracy in reproducing solutions to the problem and computational tractability for real-time control under different configurations of simulation parameters. It is found that Gaussian process regression is the most suited for use as a surrogate model for the given problem. Finally, a realistic application with multiple ride-hailing vehicle operations is presented. The proposed approach for controlling the stop positions of vehicles is able to achieve an improvement of 20.65% over the uncontrolled case. The example shows the potential of the proposed approach in reducing the negative impacts of stopping vehicles and favorable computational properties

The Driverless City

Autonomous Vehicles (AVs) are poised to become the next revolution in mobility. Marketers and engineers enthusiastically promise numerous benefits that AVs will deliver in a future without human drivers: huge reductions in accidents, parking spots, congestion, even the elimination of the loathsome commute among many others. But there are as many, if not more potential ways that the AV revolution can also go wrong: worsening traffic and congestion, urban sprawl, and eroding public transit, for example. How will Autonomous Vehicles shape cities in the future? The Driverless City is not one city: it is many. AVs could be a boon or a debacle. They could even be both at the same time. An extensive literature review revealed a broad cone of possibilities: a myriad different impacts that driverless vehicles could have on different aspects of a city. After synthesizing these into ten main areas of impact, key scenarios are expounded with supplemental foresight. This top-down approach is followed by a bottom-up research workshop where non-expert participants from the general public weighed in on the synthesis and scenarios, and expressed their own thoughts and concerns about what The Driverless City could be. Then, a group of experts helped narrow the cone of possibility into much tighter cones of probability using the Delphi research method. These forecasts and projections shine a spotlight on the key considerations that city planners, urban designers, policy makers and other decision-makers should be taking now to promote desirable outcomes for their city, and curtail undesirable ones

Integrated Simulation Platform for Quantifying the Traffic-Induced Environmental and Health Impacts

Air quality and human exposure to mobile source pollutants have become major concerns in urban transportation. Existing studies mainly focus on mitigating traffic congestion and reducing carbon footprints, with limited understanding of traffic-related health impacts from the environmental justice perspective. To address this gap, we present an innovative integrated simulation platform that models traffic-related air quality and human exposure at the microscopic level. The platform consists of five modules: SUMO for traffic modeling, MOVES for emissions modeling, a 3D grid-based dispersion model, a Matlab-based concentration visualizer, and a human exposure model. Our case study on multi-modal mobility on-demand services demonstrates that a distributed pickup strategy can reduce human cancer risk associated with PM2.5 by 33.4% compared to centralized pickup. Our platform offers quantitative results of traffic-related air quality and health impacts, useful for evaluating environmental issues and improving transportation systems management and operations strategies.Comment: 35 pages, 11 figure

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

Intelligent management of on-street parking provision for the autonomous vehicles era

The increasing degree of connectivity between vehicles and infrastructure, and the impending deployment of autonomous vehicles (AV) in urban streets, presents unique opportunities and challenges regarding the on-street parking provision for AVs. This study develops a novel simulation-optimisation approach for intelligent curbside management, based on a metaheuristic technique. The hybrid method balances curb lanes for driving or parking, aiming to minimise the average traffic delay. The model is tested using an idealised grid layout with a range of flow rates and parking policies. Results demonstrate delay decreased by 9%-27% from the benchmark case. Additionally, the traffic delay distribution shows the trade-offs between expanding road capacity and minimising traffic demand through curb management, indicating the interplay between curb parking and traffic management in the AV era

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management