Strategic Transport Planning Research Articles

Solving discrete network design problem using disjunctive constraints

AbstractThis paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency. It integrates disjunctive programming with an improved partial linearized subgradient method to enhance performance without significantly affecting solution quality. We evaluated our algorithm on the mid‐scale Sioux Falls and large‐scale Chicago networks. We assess the proposed algorithm's accuracy by examining the objective function's value, specifically the total travel time within the network. When tested on the mid‐scale Sioux Falls network, the algorithm achieved an average 46% improvement in computational efficiency, compared to the best‐performing method discussed in this paper, albeit with a 4.17% higher total travel time than the most accurate one, as the value of the objective function. In the application to the large‐scale Chicago network, the efficiency improved by an average of 99.48% while the total travel time experienced a 4.34% increase. These findings indicate that the deterministic algorithm proposed in this research improves the computational speed while presenting a limited trade‐off with solution precision. This deterministic approach offers a structured, predictable, and repeatable method for solving DNDP, which can advance transportation planning, particularly for large‐scale network applications where computational efficiency is paramount.

Introducing a Novel Framework for the Analysis and Assessment of Transport Projects in City Regions

A profound appraisal framework has been developed and refined in transport economics and planning literature for decades, mainly characterised by welfare economic theory, cost–benefit analysis, and transport demand modelling. In summary, the appraisal methodology and its applications have concentrated on single infrastructure measures, marginal impacts identified through ceteris paribus comparisons, forecasts based on trends from the past, and monetary assessments of all quantifiable impacts. However, this framework has been continuously contested in transport planning literature, for instance, for its focus on travel demand and short-term travel time savings. Therefore, we suggest a novel approach for planning and assessing transport schemes in city regions, combining accessibility analyses, quantitative target indicators, and cost-effectiveness analysis. We develop and test this approach by assessing a proposed underground rail project in the Munich city region, the U5 southeast extension. In this case, we define an accessibility target level and estimate the potential for push measures along with the U5 project. We find modest impacts on quantitative targets in the Munich city region: Even when the U5 southeast extension is bundled with push measures in selected transport cells, the contribution to passenger transport-related carbon dioxide emission targets and primary energy consumption targets is low. Nevertheless, we demonstrate that the proposed assessment framework can support strategic transport planning in city regions. We argue for a change in perspective towards supply-side-oriented urban transport planning. Our proposed methodology is a first step in a different direction towards a sustainable mobility planning paradigm.

Modeling of shared mobility services - An approach in between aggregate four-step and disaggregate agent-based approaches for strategic transport planning

Shared mobility services are slowly penetrating European cities. Hence, transport models that are capable of modeling them are fundamental, to support policy-makers for making informed decisions. Many European cities, especially the small- and the medium-sized ones, continue to use the traditional four-step modeling approach. This approach does not have the necessary capacity to model the shared mobility services and there is a need to extend them. Hence, this research proposes an extended framework, which includes modules for synthetic population generation and fleet management. Furthermore, modules are also suggested for the estimation of emissions, car-ownership and induced demand, as such measures are increasingly expected by cities. Multiple equilibrium checks between the aforementioned modules are avoided in the design of this development, to reduce model complexity and convergence issues. The models and the algorithms used for the additional modules in this study can be replaced with alternative equivalent models, provided the inputs and outputs are consistent with the framework. As such, the framework is software agnostic. Based on a case study on the city of Thessaloniki, a proof of concept is provided. The intermediate modeling framework, proposed in this study, provides an opportunity for cities to evaluate and integrate shared mobility systems and form long term planning strategies. Besides, it acts as a bridge between the worlds of simple and complex modeling approaches and pave the way for reducing the reservations of the cities toward complex approaches, while preparing them for a smoother transition in future.

Theory of Planned Behavior to Understand Commuter’s Perception towards Mass Rapid Transit in Dhaka City, Bangladesh

The Dhaka Metro Rail (DMR) has been constructed as part of the Dhaka Transport Coordination Authority’s 20-year Strategic Transport Plan to reduce traffic congestion in Dhaka, the capital city of Bangladesh. The DMR is the first urban rail transit system in Bangladesh and has the potential to change the existing modal share. Commuters have mixed responses about the daily commuting on the DMR and mode choice behavior. This study analyses the commuters’ perception of a modal shift towards the DMR by applying the theory of planned behavior (TPB). The structural equation model (SEM) used environmental concern and socio-demographic factors as additional explanatory variables to improve the explanatory capability of the TPB. A questionnaire survey was administrated using an online survey and validated via an in-person interview to collect the commuters’ responses. The results reveal that environmental concern is the most significant contributor to commuters’ perceptions of a modal shift towards the DMR in Dhaka City, followed by the attitude and perceived behavioral control of the respondents. The findings offer valuable insights for the DMR and other mass rapid transit service operators for policy-making in Dhaka and other cities with similar socio-economic demographics in the world.

Intelligent transportation systems as a factor of strategic transportation planning

The use of Intelligent Transportation Systems (ITS) in motor vehicles is anticipated to dramatically enhance both traffic safety and mobility. Successful large-scale implementation of these systems depends critically on the enthusiasm of potential users to accept them. However, market segmentation has gotten surprisingly little attention at the current level of ITS technology development, despite the fact that it is required to capture the predicted variance in demands and expectations of drivers in various categories of age, gender, country, etc. Therefore, this study's major objective was to analyze how open drivers of different demographics would be to adopting a wide variety of ITS systems with promising future safety applications.Finding out what people in cars really required in the case of an accident was an important first step. It seems that in order for ITS technology to be broadly embraced, it will be necessary to meet the demands and expectations of a wide range of driver demographics. Surprisingly low adoption rates were seen for alcohol interlocks and electronic license keys despite their effectiveness in reducing road trauma and expenses. There has been some discussion about what these findings mean for the future of ITS.

Analysis of parking traffic in Cologne, Germany, based on an extended Macroscopic transport model and parking API data

While macroscopic transport models are recognised as valuable tools for strategic transport planning, parking is rarely explicitly considered in such models. On the other hand, park-search traffic represents a significant portion of traffic in urban areas, according to many studies. Even if there are already some attempts to integrate parking into transport models, they have not found their way into the practice of modellers.The presented work enhances and combines previous approaches for modelling parking, and proposes a tool for practitioners to analyse common transport problems involving parking. It builds upon existing, even calibrated transport models, using the conventional four-step modeling technique. The approach also explores some emerging, internet-based data sources for transport models. Parking data is accessed from an Application Programming Interface (API) and embedded within the model procedure. Such new data resources resolve some problems from previous model approaches, which see the availability on parking data as problematic. With the combination of a macroscopic transport model and parking data from an API, aspects of parking supply and demand are represented within the model setting, whereas parking choice is calculated via an optimisation of a park-search route.The conceptual approach is demonstrated in the context of an existing macroscopic transport model for Cologne, Germany. This model is extended and refined to represent parking patterns within the study area. As a result, the spatial distribution and the effects of park-search traffic are explicitly shown, indicating some realistic results. It is also shown that the level of detail and the expressiveness of the existing model is increased.

Towards sustainable mobility – Transformative scenarios for 2034

Increasing the share of walking and cycling is one of the building blocks of sustainable mobility transformation. Fundamental changes are needed in society and urban structure to support the integration of walking and cycling into everyday routines. We conducted an online Delphi study with 30 walking and cycling experts during the first wave of the COVID-19 pandemic in Finland in the spring of 2020. The aim was to explore transport experts’ views on the future and derive scenarios of walking and cycling with the broader context of the urban mobility system. The scenarios were created using a combination of cluster analysis and qualitative content analysis. In addition, a methodological elaboration of the Disaggregative Delphi analysis was introduced, systematising the analysis of qualitative data. The analysis resulted in five scenarios depicting walking and cycling in 2034: 1. Business as Usual Plus, 2. The Demise of Bus-Transport, 3. The Era of Soft Modes, 4. Public Transport Serving All, and 5. Car-dependent Lifestyles Persist. The scenario set can be used as a basis for strategic transport planning and policy as well as a more practical tool for identifying measures for walking and cycling promotion in urban areas.

Transformative capacity for climate mitigation in strategic transport planning – principles and practices in cross-sectoral collaboration

ABSTRACT This article presents findings from a qualitative in-depth analysis of a four-year Swedish national policy initiative where six public agencies were commissioned to produce a strategic plan for a transition towards a fossil-free transport sector. The aim of the article is to provide empirically grounded insights on principles and practices of importance for building transformative capacity in strategic, long-term transport planning. In the analysis, the concepts stewarding, unlocking, transforming and orchestrating are applied to explore and discuss transformative features of the policy initiative. Altogether, several elements of transformative capacity were developed through the process. Of specific importance was the establishment of an open and explorative approach to carrying out the commission, and ways in which the organizations involved started to challenge dominant perspectives and analytical practices in conventional transport planning. Shared principles and practices for analysis and assessment were developed, which allowed for a broadened consideration of climate mitigation measures. However, due to a lack of coordination with formalized planning settings and a lack of political decisions to sustain the commission, there are yet no signs of the work influencing conventional transport planning. Even so, gained experience and insights from this case can inform future change-oriented initiatives.

ANALYZING PUBLIC TRAVEL DEMAND BY A FUZZY ANALYTIC HIERARCHY PROCESS MODEL FOR SUPPORTING TRANSPORT PLANNING

Travel demand plays an essential role in strategic transport planning. Generally, experts use either discrete methods, e.g. discrete choice models or simulation, e.g. activity-based models to estimate demand in transportation. This paper offers a different solution; instead of using the traditional approach, the demand is considered as a Multi Criteria Decision Making (MCDM) problem and surveying the citizens’ preferences provides the results for decision support. Public transport demand depends on two main issues, quality and price of the transportation. In a hierarchical model, both issues have been integrated and the well-proven Analytic Hierarchical Process (AHP) method has been applied in the current research. Further, fuzzyfication of the scores have also been conducted because of the citizen evaluator pattern. The fuzzy-AHP (FAHP) model has been tested in a real-world situation with the case study of Amman (Jordan).

Assessing Long-Term Impacts of Automation on Freight Transport and Logistics Networks: Large-Scale LRP Integrated in Microscopic Transport Simulation

Up to now, bulk transports have been carried out via a hub-and-spoke network in the general cargo sector. However, it is expected that the use of autonomous vehicles will enable a more flexible delivery. Such developments may, economically, make sense for shippers. From an ecological point of view, also negative effects can be expected due to enhanced transport performance. In the framework of this research, we investigate the impacts of automation on general cargo transport at the logistics network level. For assessing the impacts of autonomous vehicles on logistics network structures and on freight transport routes ex-ante, an instrument for strategic transport and logistics network planning is needed. We develop an effective heuristic to find new facilities and adjust the network, while thereby considering the routing characteristics by tackling the large-scale location routing problem (LRP). By the linked approach, we can optimize the logistics network and also measure exact transport distances, driving transport lead times and number of necessary vehicles on the infrastructure network. We operationalize this approach in the framework of a case study focusing on the food retail distribution in Germany. In fact, this research reveals that the utilization of autonomous vehicles significantly enhances transportation ranges and the number of tours, while reducing the number of operating facilities.

Vehicle scheduling for on-demand vehicle fleets in macroscopic travel demand models

The planning of on-demand services requires the formation of vehicle schedules consisting of service trips and empty trips. This paper presents an algorithm for building vehicle schedules that uses time-dependent demand matrices (= service trips) as input and determines time-dependent empty trip matrices and the number of required vehicles as a result. The presented approach is intended for long-term, strategic transport planning. For this purpose, it provides planners with an estimate of vehicle fleet size and distance travelled by on-demand services. The algorithm can be applied to integer and non-integer demand matrices and is therefore particularly suitable for macroscopic travel demand models. Two case studies illustrate potential applications of the algorithm and feature that on-demand services can be considered in macroscopic travel demand models.

Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area

In strategic transport planning, rail stations play different roles at different scales, particularly in large cities and metropolitan regions, where movement structure shows polycentric patterns. However, there is a general lack of evidence-based studies investigating the relative network roles of rail stations. Therefore, this paper aims to explore rail stations' roles within and beyond metropolitan communities in London. Drawing on the Transport for London's newly released NUMBAT passenger origin-destination data, we unravel the travel structure and identify the stations' roles through network methods, community detection and Z-P scores. Our research has three results: First, London's passenger movement network can be subdivided into seven communities. The majority of stations within the same communities are geographically localised with few exceptions. Second, stations play distinct roles in handling the patterns of movement at different scales. Typically, there exists a set of stations that are significant for connecting between metropolitan communities, because the disruption of those stations will have a greater impact on overall network efficiency and affect a larger and more diverse group of passengers. Third, a classification of rail stations is developed based on stations' commitment at two scales. This classification allows planners to have a finer level of understanding of the network role of the stations and thus develop and adjust tailored (re)development strategies.

A Data-Driven Quasi-Dynamic Traffic Assignment Model Integrating Multi-Source Traffic Sensor Data on the Expressway Network

Static traffic assignment (STA) models have been widely utilized in the field of strategic transport planning. However, STA models cannot fully represent the dynamic road conditions and suffer from inaccurate assignment during traffic congestion. At the same time, an increasing number of installed sensors have become an important means of detecting dynamic road conditions. To address the shortcomings of STA models, we integrate multi-source traffic sensor datasets and propose a novel data-driven quasi-dynamic traffic assignment model, named DQ-DTA. In this model, records of toll stations are used for time-varying travel demand estimation. GPS trajectory datasets of vehicles are further used to calculate the dynamic link costs of the road network, replacing the imprecise Bureau of Public Roads (BPR) function. Moreover, license plate recognition (LPR) data are used to design a statistical probability-based multipath assignment method to capture travelers’ route choices. The expressway network in the Hunan province is selected as the study area, and several classic STA models are also chosen for performance comparison. Experimental results demonstrate that the accuracy of the proposed DQ-DTA model is about 6% higher than that of the chosen STA models.

ІНДИКАТОРИ СТАЛОЇ МІСЬКОЇ МОБІЛЬНОСТІ

The article deals with the sustainable urban mobility indicators framework. Currently, over half of the world’s population resides in cities. This urbanization trend is expected to continue and more than 80 per cent of humanity is expected to live cities by 2050. The urbanization process is accompanied by economic growth, but also drives the consumption of materials and energy, the production of waste, and the emission of greenhouse gases and impacts city mobility quality. Sustainable development is the main paradigm of modern city development which presupposes a balance between the needs of the present and development opportunities for future generations. Strategic transport planning in modern European cities takes the form of developing and implementing Sustainable Urban Mobility Plans. Ukrainian cities do not stand aside from European trends and are gradually developing and implementing urban mobility plans. An integral part of transport planning is the development of indicators of sustainable urban mobility. Sustainable mobility indicators provide information on current trends in the development of the society, city system viability, and the functioning of its elements. The development of effective, easily calculated, and interpreted indicators will reduce public costs and increase the efficiency of strategic transportation planning. The development of indicators of sustainable urban mobility involves careful analysis and systematization of data and finally results in indicators frameworks development. Sustainable mobility indicators frameworks based on important relationships between infrastructure needs and the environment, infrastructure and transportation impacts on the economy, environment, and social well-being. These frameworks can be used to help agencies refine their visions as well as develop policies, planning procedures, and measurement and monitoring systems for achieving sustainable city mobility. The development of such frameworks for Ukrainian cities requires the study of international experience and taking into account the national characteristics of the country.

Is neighborhood level Jobs-Housing Balance associated with travel behavior of commuters?: a case study on Dhaka City, Bangladesh

Abstract Being one of the densely populated cities of the world, city dwellers of Dhaka have to face severe traffic congestion daily while commuting for different purposes. According to the World Bank report, Dhaka is losing around 3.2 billion working hours daily as the current average driving speed is about seven kilometers per hour. To ease traffic congestion, urban policymakers around the world are concentrating on the geographical balance between the locations of jobs and housing. Despite the apparent acceptance of jobs-housing balance as a policy tool to guide urban development, little empirical research has been carried out on jobs-housing balance and its relation to the travel behavior of the residents. This study aims to close this research gap by: (a) quantifying neighborhoodlevel jobs-housing balance; (b) investigating whether there are any significant differences in commuting time and distance of the resident workers in correspondence with different Job-Housing Ratio (JHR) values of neighbourhoods. This paper uses Traffic Analysis Zones (TAZs) demarcated on the Revised Strategic Transport Plan as a unit of measuring the Job-Housing Ratio, and then, TAZs have been categorized into three groups, named as Housing-rich, Balanced, and Employment-rich, in terms of the recommended range of Job-Housing Ratio. Residents’ home-based commuting data have been calculated from 16,000 households who participated in Household Interview Survey of Revised Strategic Transport Plan 2015. Results demonstrate that Housing-rich neighbourhoods have a significantly longer commuting distance and time than both Balanced and Employment-rich neighbourhoods. Above all, both commuting time and distance show exponentially declining relation, but with a decreasing rate, in correspondence with JHR. The study output suggests that the achievement of a balance between jobs and housing in a neighborhood would be beneficial for the people to economize the commuting time and distance.

Path-Based Dynamic User Equilibrium Model with Applications to Strategic Transportation Planning

This study proposes an analytical capacity constrained dynamic traffic assignment (DTA) model along with an efficient path-based algorithm. The model can be applied to analyzing dynamic traffic demand management (TDM) strategies, but its specific feature is allowing for an evaluation of advanced traveler information systems (ATIS) within the strategic transportation planning framework. It is an extension of a former DTA model, where each link is given an infinite capacity and is assumed to be completely traversed inside any time interval it is reach by a path. Thereby, the length of time intervals should be very longer than the link travel times, and the link capacity constraints are overlooked. The paper rests on three key ideas to overcome these restrictions: (1) adding path-link fraction variables to the base model, allowing path flows to spread out over time intervals on long links; (2) uniformly dividing each link into smaller parts (segments), so that each part is more likely to be traversed inside a time interval; (3) imposing a dynamic penalty function on each link, thereby the capacity constraint can be included. The proposed DTA algorithm decomposes the augmented model in terms of origin-destination (OD) pairs and departure time intervals, and utilizes a dynamic column generation technique for generating active paths between the OD pairs. The optimal solution to a one-link network demonstrates that the model is able to approximate the dynamic flow propagation over a link with sensible accuracy. Besides, investigation of the results for a small test network reveals that the algorithm performs very well in computing temporal link flows and queuing delays. Finally, numerical experiments on a real life network indicate that the algorithm converges sufficiently fast and provides path information for each time interval. The network is further used to show the algorithm is capable of assessing a dynamic TDM strategy as well as an ATIS system.

Method for an economical assessment of urban transport systems

This paper presents an intermodal method for identifying municipal expenses and revenues in the transport sector and allocates them to the urban transport systems: pedestrian, bicycle, car, truck traffic and local public transport. The method is based on full cost accounting, in that the total transport-related costs are allocated to the urban transport systems based on a top-down-approach. The method is centred on the development of allocation keys and attribution factors based on scientific engineering findings. For a complete economic comparison, various assessment methods are presented, taking the most important transport-related external effects into account (accident costs, air pollution costs, climate change costs, noise costs and health benefits in walking and cycling). The external effects have been based on existing national and international methods, which are monetised with corresponding (accepted) cost factors from the appropriate scientific literature. The method allows cost transparency and determines economic indicators that can serve as a basis for discussion and decision-making in the allocation of funds for the different urban transport systems. Besides that, it can be used directly to seek out goal indicators in urban development and transportation planning. With the approach presented here, for the first time municipalities will be able to have a complete overview of their transport-related revenues, expenses and external effects, differentiated each by urban transport system. This results in an additional and important instrument for strategic transportation planning and a next step on the road to ‘true costs in the transport sector’.

DETERMINANTS OF EFFECTIVE IMPLEMENTATION OF SUPPLY CHAIN MANAGEMENT PRACTICES IN THE DISTRIBUTION OF CONSUMABLE PRODUCTS IN THE OIL INDUSTRY IN KENYA: A CASE OF VANOIL ENERGY LIMITED

Purpose: The study sought to examine the challenges faced in implementing effective supply chain management practices during the distribution of the consumable products in the oil industry in Kenya with reference to Vanoil Energy Limited. The study was to establish how warehousing capabilities, strategic supplier relationships, transportation planning and demand and supply forecasting affect distribution performance of the oil industry. To achieve this, the researcher reviewed both theoretical and empirical literature and proposed the research methodology that addressed the gaps identified in literature as well as answer the stipulated research questions.Methodology: This research study adopted a descriptive research design approach. The researcher prefers this method because it allows an in-depth study of the subject. A census was conducted where all the 70 staff in procurement department were issued with questionnaires. Data was collected using self-administered questionnaires. The data collected was analysed by use of descriptive and inferential statistics. The quantitative data generated was keyed in and analysed by use of Statistical Package of Social Sciences (SPSS) version 22 to generate information which will be presented using tables, charts, frequencies and percentages. Multiple regression models were used to show the relationship between the dependent variable and the four independent variables in this study.Results: The findings of the study indicated that warehousing capabilities, strategic supplier relationships, transport planning and demand and supply forecasting have a positive relationship with effective distribution of consumable products in the oil industry.Recommendations: Finally, the study recommended that companies in the oil industry should embrace supply chain management practices so as to improve distribution performance and further researches should to be carried out in other institutions to find out if the same results can be obtained.

Considering landscape in strategic transport planning

The implementation of transport infrastructure plans often has significant impacts on landscapes, especially where new roads and railroads are built. Key decisions regarding the building of new transport infrastructures are often made on a strategic level, where the long-term development of a region is determined, and before the infrastructure project actually begins. In this paper we build on previous advances in Strategic Environmental Assessment theory by linking the process-related issues of the integration of these assessments in general to landscape issues in particular; we use a multiple case study of Swedish transport planning.Results of this study indicate that the particular planning processes we looked at failed to carry out strategic landscape assessments and integrate landscape assessments in the planning process. We conclude that this can be explained by the flawed procedure of assessing landscape, the unhelpful structuring of SEA reports and by process-related issues. The idea of applying a holistic understanding of landscape, in line with the ELC, was notably absent from the studied cases. The lack of consideration of landscape as a whole can be attributed to poor use of dissipated and fragmented knowledge about landscapes as well as weaknesses in the assessment procedure. Our results indicate that the traditions of EIA are still prevalent in the practice of SEA, despite the fact that SEA theory has moved away from EIA-based methodology to become a tool for integrating environmental concerns into decision-making and for paying close attention to strategic decision processes.

Path-based capacity-restrained dynamic traffic assignment algorithm

ABSTRACTThis study proposes a new fast convergence path-based dynamic traffic assignment algorithm. The algorithm is based on an analytical capacity-restrained dynamic user equilibrium model, in which link travel times are temporal functions of link flows. In previous models the length of time intervals has to be much longer (about five times) than the link travel times in each time interval using the simplifying assumption that each link will be fully traversed within the time interval that a path reaches its tail node. Otherwise, these models will make large errors and will exhibit difficulties in attaining optimality. This paper tackles the problem by proposing a systematic way of link segmentation. The numerical and case study results of the proposed algorithm show its excellent performance. The algorithm rapidly converges to optimality and provides path information over time intervals, which make it suitable for evaluating the intelligent transportation systems in strategic transport planning.