Sustainable Transport Planning Research Articles

Telecommuting and Travel Behaviour: A Survey of White-Collar Employees in Adelaide, Australia

COVID-19 prompted a significant number of employees to shift to remote work for the first time, potentially reshaping future work patterns. This study examines the post-COVID impacts on telecommuting, commuting behaviour, travel activities, and lifestyles in the city of Adelaide, South Australia. A multivariate nominal logistic regression analysis of 305 post-restriction survey responses revealed that home distance from the CBD, industry, occupation, and income significantly influence post-COVID telecommuting trends. Individuals living over 20 kilometres from the CBD, those in professional or managerial roles, and higher-income earners (>125k) are more prone to regular telecommuting, highlighting the impact of commute lengths, job flexibility, and financial resources on the ability to work remotely. The study revealed a higher adoption of telecommuting post-COVID, with more individuals working from home and telecommuting more often each week. This led to reduced usage of private cars and public transport, indicating a decrease in overall travel frequency. Respondents also adopted flexible work schedules, resulting in fewer peak-hour commutes, which would have resulted in lower congestion and emissions and led to more sustainable travel practices. The study also investigated future telecommuting perspectives, revealing a preference for remote work 3–4 days a week. Some respondents who initially could not telecommute have since considered it feasible and want to adopt it. Notably, about 25% of respondents would even change jobs for flexible, home-based work arrangements. The study’s results suggest that remote work frequency may influence individuals’ future house location preferences. These findings offer valuable insights for sustainable transport and urban planning considerations in the post-COVID era.

Travel behaviour and the 15-min City: Access intensity, sufficiency, and non-work car use in Toronto

One of the key travel behavioural assumptions in the 15-min City concept is that if daily necessities are nearby, residents would be encouraged to use slower but more sustainable modes, such as walking, cycling and public transit to reach these destinations, thereby reducing car dependence. This research explores non-work car use associated with the 15-min City concept in the City of Toronto, Canada. We first calculate transportation accessibility, or what we refer to as access intensity, to five categories of daily necessities (food, commercial, health, recreation, and education establishments) using walking, cycling, public transit, and driving. Next, these results are analyzed using a set of minimum access criteria to particular amenities within the different destination types to determine a set of binary access or sufficiency scores. Spatial patterns of accessibilities by mode show expected pockets of high and low access. However, further analysis of non-work trip rates using travel survey data suggests that increases in 15-min accessibilities by walking, cycling, and transit are associated with decreases in the use of driving for 15-min trips. In particular, driving rates decrease as sufficient walking, cycling, and transit access improves with the largest decrease associated with sufficient walking access to all five categories of necessities. This work offers important implications for sustainable transportation and land use planning as it appears that residents do use alternative and more sustainable modes when they are associated with sufficient accessibility to all categories of destinations.

An optimisation model for sustainable multi-commodity transportation planning

This paper aims to establish a supply chain model that significantly reduces economic and environmental costs. It comprises all activities related to procurement, production, and distribution planning. The proposed multi-objective multi-commodity optimisation model deals with the four conflicting objectives of reducing costs and emissions and choosing top-priority suppliers and the most efficient vehicles. We apply an integrated AHP (analytic hierarchy process) and TOPSIS (technique for order preference by similarity to an ideal solution) technique to determine the weights of suppliers, depending on three indices of criteria, alternatives, and raw material. This paper proposes a cross-efficiency evaluation method using data envelopment analysis (DEA) to ensure that the cross-evaluation of different types of vehicles for evaluating peers is as consistent as possible. The mutually contradictory objectives give rise to several Pareto-optimal solutions. The optimal compromise solutions are found using a lexicographic goal programming technique. We present a real-world case to demonstrate the effectiveness of the proposed methodology, followed by numerical comparisons and additional insights.

AI-IOT-Based Adaptive Control Techniques for Electric Vehicles

The convergence of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has transformed the field of sustainable transportation planning, notably in the context of Electric Vehicles (EVs). The Problems in all the Existing models are not having proper coordination of optimizing the elements of EV functioning. This research proposes a novel strategy to improving sustainable transportation planning by utilizing AI and IoT. AI-powered algorithms analyze real-time data from IoT sensors installed in EVs and the surrounding environment. These adaptive control algorithms are designed to solve issues including range anxiety, charging infrastructure optimization and energy efficiency. Predictive analytics, route optimization, energy management, and grid interface are all part of the proposed system. Energy management algorithms alter EV settings dynamically to maximize efficiency while taking into account real-time traffic conditions thereby increasing the range extension is upto 2.5% and the total energy efficiency is improved upto 92%. Furthermore, bidirectional connection allowed by IoT devices facilitates the integration of EVs into the energy grid. EVs may engage intelligently in Vehicle-to-Grid (V2G) interactions, providing grid services such as energy delivery during peak demand periods and grid stability.

Sustainable Urban Mobility Planning in the Port Areas: A Case Study

Sustainable development, urban planning, mobility, and transport planning, integrated within the context of sustainable urban mobility, have been central themes in both scientific and applied spheres over the past few decades. In port cities, it becomes particularly essential to tackle sustainability issues given the pollution and noise emanating from ships and other port-related activities. To meet mobility and transportation sustainability needs in the port area, a port should implement measures aligned with a sustainable urban mobility planning (SUMP) approach. However, many ports have thus far achieved limited results in this direction due to the absence of an approach to defining sustainable mobility solutions based on the SUMP approach for an urban area associated with the given port. The overall aim of this paper is to support the development of territorial SUMP for port areas by proposing a methodology that identifies and prioritizes sustainable mobility solutions tailored to a specific port area. The proposed methodology is applied in the Port of Bar (Montenegro) through an appropriate case study. In this case study, the methodological steps are systematically followed, resulting in the practical implementation of the selected mobility solution: the use of a hybrid bus for internal employee transportation within the port area. The undertaken case study underscores the simplicity, practical applicability, and adaptability of the proposed methodology.

Examining active travel behavior through explainable machine learning: Insights from Beijing, China

Active travel, namely walking and cycling, is an eco-friendly and socially beneficial mode of sustainable transportation. However, existing research on active travel relies on limited survey data and generalized linear models. To fill the gap, our study integrates large-scale big trip data and data-driven machine learning to simultaneously predict active travel flow and probability. We employ SHapley Additive exPlanation to analyze the nonlinear effects of various characteristics (e.g., travel, socioeconomic, infrastructure, environment) on active travel. Gradient Boosting Decision Tree performs best for both prediction tasks. The overall importance of travel distance is over 50% to the model. Features like crow-fly distance, housing price, point-of-interest density, subway proximity, building area/road density, and urban greenery exhibit pronounced nonlinear effects. Local interpretability analysis reveals the determinants of specific trips, facilitating targeted optimization implications. Our study reveals the drivers and nonlinearities of active travel behavior and aids sustainable transportation planning.

Computational Architecture of an Integrated Urban Model Considering Physical-Virtual Activity Spaces

This study presents the computational architecture of the integrated transport, land-use, and emission (iTLE) modeling system. It incorporates the effects of physical-virtual activity-space interactions within the microsimulation framework, making it a comprehensive tool for predicting travel demand and evaluating the impacts of evolving activity-travel behavior on transport and land-use systems. The model systematically integrates households’ and individuals’ long-term, medium-term, and short-term travel related decisions, and traffic assignment allowing for more reliable estimates of travel demand. Application of the iTLE prototype in the Halifax Regional Municipality (HRM), Canada reveals notable trends, including a reduction in travel time for work activities, highlighting the potential of teleworking as a pragmatic strategy for traffic management. The model’s flexibility is demonstrated through its ability to capture the trade-offs between physical and virtual activity environments, aligning with the intricacies of modern digitalized societies. Integration of iTLE with the traffic assignment system enhances its applicability across diverse urban contexts, streamlining the process of setting origin-destination trip matrices and significantly improving computational efficiency. By systematically coupling the micro-behavioral decision processes, iTLE generates robust estimates of traffic flow, emissions, and energy use, providing reliable insights for sustainable and efficient transportation planning. This study contributes to advancing integrated urban modeling, offering a valuable tool that can be replicable for multiple cities in Canada and beyond.

Revolutionizing Mobility: Big Data Applications in Transport Planning

Revolutionizing Mobility: Big Data Applications in Transport Planning

Simulation–optimisation approach for sustainable planning of intermodal logistics in the Brazilian grain export industry

Planning nationwide sustainable logistics for grain exports at a tactical level assumes flexibility in handling uncertainties. Despite the dynamic operations, the solution’s approach focuses mainly on optimisation, leaving out the queuing requirements. This paper presents a simulation–optimisation approach for planning the Brazilian intermodal grain export chain by integrating trucks, railways, and port terminal operations. We evaluate a base scenario, aiming to minimise CO2 emissions in rail transport, meeting demand requirements, and three alternative planning scenarios concerning demand retraction, trend, and expansion. The sustainable grain transportation plan of the base scenario shows a 66.8 kilo tons CO2/year emission reduction while increasing the exports by 2.51% (approximately 146 kilo tons grains/year) and a contribution to profit of $43.82 million in the short term. Scenario analyses open discussions about sustainable investments in intermodal transport for the medium and long term. The proposed approach is suitable for other commodity chains, representing a step towards consistently applying environmentally oriented supply chain planning.

Examining associations with on-time performance and identifying relevant road network, demographic, socioeconomic and land use characteristics within the bus stop vicinity for proactive and reliable public transportation system planning

Growing population, rapid urbanization, and increasing travel demand emphasize the need for reliable public transportation systems and sustainable transportation planning. A reliable bus service fosters a more significant, satisfied, and committed base of users. This research focuses on examining the association between on-time performance (OTP) and road network, demographic, socioeconomic, and land use characteristics to identify relevant external factors for proactive and reliable public transportation system planning. The analysis was conducted at a bus stop level. Bus arrival/departure data from the Charlotte Area Transit System (CATS) was obtained. The road network, demographic, socioeconomic, and land use characteristics were captured within 0.25-mile and 0.50-mile buffers. Pearson correlation analysis was conducted to understand the association between OTP and road network, demographic, socioeconomic, and land use characteristics by day of the week and time of the day. The results show that OTP is associated with external factors such as the number of signalized or cul-de-sac/dead-end intersections, number of lanes, network density, population, income (median and total), and land use types related to residential and commercial/employment purposes within the bus stop vicinity. The findings provide vital insights for transit agencies to enhance scheduling, service and maximize benefits, effectively utilize available resources, plan, and provide equitable services to all riders.

Green Distribution Route Optimization of Medical Relief Supplies Based on Improved NSGA-II Algorithm under Dual-Uncertainty

With growing concerns about environmental issues, sustainable transport schemes are receiving more attention than ever before. Reducing pollutant emissions during vehicle driving is an essential way of achieving sustainable transport plans. To achieve sustainable transport and reduce carbon emissions, on the premise of ensuring rescue timeliness, this research proposes a multi-objective distribution route optimization model considering the minimization of transportation cost and transportation risk under dual-uncertainty constraints, providing a practical framework for determining the optimal location of rescue centers and distribution routes in emergencies using fuzzy theory. First, this paper proposes objective functions that innovatively take into account the congestion risk and accident risk during the distribution of medical supplies while introducing the carbon emission cost into the transportation cost and using the fuzzy demand for supplies and the fuzzy traffic flow on the roads as uncertainty constraints. Then, this paper designs a multi-strategy hybrid nondominated sorting genetic algorithm (MHNSGA-II) based on the original form to solve the model. MHNSGA-II adapts a two-stage real number coding method for chromosomes and optimizes the population initialization, crowding distances selection, and crossover and mutation probability calculation methods. The relevant case analysis demonstrates that, compared with the original NSGA-II, MHNSGA-II can decrease the transportation cost and transportation risk by 42.55% and 5.73%, respectively. The sensitivity analysis verifies the validity and rationality of the proposed model. The proposed framework can assist decision makers in emergency logistics rescue.

Improving land-use-based urbanism through award-winning planning practice and scholarship: a North American case study

Metropolises in the Sunbelt region of the USA are in need of sensitive land-use-based urbanism comprised of more compact development, sustainable transportation, efficient infrastructure, low-carbon energy, and greenway and environmental planning. Sustainable urbanity can be partially accomplished via co-creative planning pedagogies, which in turn can lead to place-and evidence-based planning practice and scholarship. However, student work is often underappreciated because academia does not create the visibility that it requires. Planning studios and other applied disciplines are invaluable opportunities to conduct in loco research and to produce professional or near-professional documents. Town-gown collaborative projects offer many rewards. This article showcases how various undergraduate and graduate disciplines offered at Arizona State University resulted in state-level professional planning awards. It is stated that students ought to go beyond the delivery of planning documents to their instructors and clients to also seek recognition from their professional associations. More encompassing recognition of award-winning work is sure to receive further visibility not only from professionals but also from the community at large. The methodology comprised mostly qualitative policy evaluation of plan making processes. A set of lessons learned and implications is offered for those who wish to venture out in this type of academic collaborations, namely instructors, students, stakeholders, university administrators, elected officials, and professional planners. The key finding is that the land-use-based urbanism strategies discussed in the paper can encourage land preservation in arid cities and above all increase the quality of urban life in desert environments.

Enhancing urban public space through appropriate sustainable mobility policies. A multi-criteria analysis approach

Decision-making in the context of urban sustainability is a challenging problem for policy-makers, given its complex nature, demanding the compromise between different and, often, conflicting social, economic and environmental criteria. This multi-factor problem needs to be addressed in a holistic way, on the basis of an integrated transport and urban planning approach, through ad hoc planning for each different area. Land use and mobility policies are characterized by a high level of interaction, a fact that needs to be taken into account in urban and transport planning within the framework of sustainability. Effective land use policies may have a positive impact on sustainable mobility and vice versa: effective mobility policies may have a positive impact on sustainable land use in urban areas. Aiming at developing and testing via a case study a decision-support tool for policy-makers and decision-analysts to promote sustainable urban and transport planning, the application of two hybrid multi-criteria analysis models is proposed in this research work, in order to rank land use and transportation policies in the framework of urban sustainability, especially in terms of public space. The center of Thessaloniki (Greece), an urban area suffering from sustainable mobility and public space problems, is selected as a case study.

Comprehensive Energy Modeling Framework for Multi-Powertrain Bus Transit Systems

With the increasing emphasis on sustainable transportation solutions, there is an urgent need for a system that can accurately assess vehicle-specific energy consumption in real-time. However, the current modeling frameworks have a significant gap in that they are not demand- and congestion-responsive, nor are they capable of modeling the system-wide energy consumption of bus fleets at the vehicle-level, encompassing all three types of powertrains—conventional, hybrid, and electric. To address this gap, we present a comprehensive modeling framework that takes into account passenger loading, considers the dynamics of the system including acceleration, and is capable of predicting the real-time energy usage of each bus in a transit network. Using the Pioneer Valley Transit Authority system as a case study, we calibrated existing powertrain-specific models for conventional diesel, hybrid, and battery electric buses. Our demand- and congestion-responsive energy consumption modeling framework fills this critical gap and offers significant potential for future sustainable transportation planning. Relying on detailed trajectories computed from one month of bus location data, along with passenger counts and road grade, our framework is a novel contribution in that it provides spatiotemporal energy predictions for an entire bus system. As it is readily calibrated with potential improvements in accuracy given more data, this framework can be applied by transit agencies for the purposes of real-time energy consumption monitoring, fleet electrification planning, equity analyses, and carbon emission reduction strategies.

The centrality of Italian airports before and after the COVID-19 period: what happened?

Air transport in Italy accounts for around 1,8% of national production and employment. This sector has undergone a series of transformations during the recent pandemic period. The main focus of this research is the analysis of the network of national airports with more than 50.000 passengers per year both outbound and inbound and the estimation of their centrality in the presence of significant exogenous events such as the recent pandemic. The change in centrality was examined over the period 2019-2021. The methods of network analysis were applied and the results show an increased and growing centrality of some southern airports in the network, a downsizing of Rome Fiumicino airport during the pandemic period and a marginalisation in the national network of some smaller airports in the centre-north. This first step in the research lays the foundations for better actions to promote sustainable transport planning, considering local authorities, airport managers and airlines.

Applications of Terrestrial Laser Scanner in Detecting Pavement Surface Defects

An entire roadway system represents a crucial element in the sustainable urban transportation planning process. Pavement surfaces are at continual risk of accumulating serious deteriorations and defects throughout their service life due to traffic loading and environmental impact. Since roadway networks are growing rapidly, relying on visual pavement inspection is not always feasible. Therefore, this paper proposes an effective assessment method for evaluating flexible pavement surface distresses using a terrestrial laser scanner (TLS) and calculating the pavement condition index (PCI). The proposed terrestrial laser scanner method results in road condition assessments becoming faster, safer, and more systematic. It also aims to determine the geometric characteristics of the investigated roads. A major road in Egypt was selected to test the proposed technique and compare it with the traditional visual inspection method. The evaluation was carried out to assess different types of pavement distress, such as cracking, rutting, potholes, and raveling distresses. Every pavement distress was defined in terms of surface area, the width of the crack, and intensity, and the data from TLS were then processed by MAGNET COLLAGE software. A MATLAB program was developed to match the TLS observational data to plane equations. PAVER software was also used to determine the PCI values for each TLS position. The revealed distresses for the investigated road using TLS observations reveal a significant improvement in determining flexible pavement distresses and geometric characteristics.

The sustainable transport planning index: A tool for the sustainable implementation of public transportation

AbstractThe transportation sector contributes significantly to global greenhouse gas emissions, so it is crucial to assess and measure the sustainability of transportation systems. In this context, this study was conducted to develop an integrated index through the use of the multi‐criteria decision analysis method. The method combines existing discrete indexes into one comprehensive evaluation of public transportation, resulting in the sustainable transport planning index (STPI). In the STPI model, sustainability of transportation systems is assessed based on social, economic, and environmental factors that support the implementation of zero emission busses. The weight of each indicator is determined through the analytical hierarchy process, where expert judgment is used to assess the relative importance of each indicator. Normalization of indicators is performed to ensure comparability and consistency. The final STPI index is calculated as the weighted average of the normalized indicators. The STPI model reduces bias in the decision‐making process by considering multiple aspects and utilizing a structured approach to transport planning. The results of this method can provide valuable insights for decision‐makers, public transport agencies, government ministries, the private sector, and other stakeholders. As case study model, the STPI model was applied to the public transport system of the United Kingdom from 2007 to 2019, however; the methodology and lessons learned are applicable to all countries that are in the process of assembling data sets to weigh trade‐offs and inclusions in relation to sustainable transit such as accessibility and health impacts.

Traffic assignment model for sustainable transportation planning of cities-An overview

Traffic assignment model for sustainable transportation planning of cities-An overview

What is significant for engagement in cycling and walking in South Korea? Applying value-belief-norm theory

The value-belief-norm (VBN) theory has been widely used to describe pro-sustainability behavior. Despite being used to examine issues of sustainable and public transport behavior, research using the VBN has largely overlooked the pro-environmental behavior of cyclists and walkers, which is extremely significant given the role of active transport in environmental and human health. Therefore, this study creates and verifies a conceptually comprehensive active transport (cycling/walking) research model employing VBN theory, with a moderating role of cycling/walking and comparing cyclists and walkers, and utilizing PLS-SEM, multi-group analysis (MGA), necessary condition analysis (NCA), and fuzzy set Qualitative Comparative Analysis (fsQCA). Results revealed that value has a great effect on belief in walking/biking. However, belief has a stronger impact on personal norm than social norm, and also personal norm shows twice the impact on behavioral intention than social norm. Behavior to engage in cycling and walking is greatly influenced by behavioral intention. Walkers have a stronger relationship between belief and personal norm than cyclists. Cyclists have higher prediction power with social norm, while walkers have a higher prediction power with belief, personal norm, behavioral intention, and behavior. Importantly for sustainable transport planning and interventions, the cycling and walking groups distinctively different from each other in terms of solutions for outcome high level of active transport behavior.

Township Development and Transport Hub Level: Analysis by Remote Sensing of Nighttime Light

The coordinated development of township and city transportation is expected to reach new heights in the global sustainable transport plans of emerging economies. However, few studies have focused on the transport hub features considering marginal administrative division. This study examines the correlation between township development and hub level by using remote sensing of nightlight imagery. Systematically corrected satellite images of Global NPP-VIIRS Nighttime lights were selected as experimental data. Furthermore, the township hub level model and nighttime light indices were established to demonstrate the correlation characteristics of 6671 townships. Results show that the development level of road transport for a considerable number of townships is positively correlated with the hub level. The positively correlated townships show a spatial clustering distribution. In contrast, several negative correlations and random townships are related to the radiation of adjacent city growth poles and township special industrial characteristics. Nighttime light data can compensate for the difficulty in obtaining socioeconomic data below the prefecture level from a multiscale micro perspective and statistical caliber differences. These findings can be proven to be valuable to planners and designers of township development and regional transport.