Urban wholesale market relocations often impose additional travel burdens on mobile vendors operating at low margins in the informal sector and create spatial inequities when guided by non-spatial criteria. This study, aiming at assessing the impact of market relocations on mobile vendors, presents a novel GIS-based framework that combines the Clarke and Wright Savings Algorithm for route optimisation with Kernel Density Estimation, a machine learning technique, to identify high intensity vendor corridors. Heatmap values of vendor activity and average distance metrics were normalised and candidate market sites were ranked using a new Equity-Efficiency Index measure which accounts for landuse policy preferences. Candidate sites were mapped further considering gain/loss in accessibility, for classifying the service area into improvement and degradation zones weighted by population. A case study based on the relocation of Segiri market in Samarinda, Indonesia has been developed to illustrate the method. Results show that there is a new location in Samarinda that can deliver large efficiency gains in key corridors but resulting in widespread service area losses. Other potential locations achieve moderate, evenly distributed accessibility improvements. The framework delivers spatially explicit evidence of benefit and burden distribution and supports relocation decisions that balance logistical efficiency, equitable access and urban resilience.

Connected Autonomous Vehicle (CAV) is a new technology that can operate without human intervention and able to communicate with other CAVs and connected infrastructure. Due to its ability, CAV is expected to create significant change in traffic management and creates benefits e.g., increasing road traffic performance, road safety improvement. Before CAV has a significant share on the road, there will be a transition period where CAV co-exists with normal vehicle (NV). The purpose of this study is therefore to develop a tool, based on cell transmission model (CTM), to assess the impact of CAV in mixed traffic. We name the model as multiclass first-in-first-out cell transmission model (MF-CTM). The model has three distinct features, namely: i) First-in-First-Out (FIFO), ii) Dynamic Maximum Flow Rate, and iii) Dynamic Maximum Cell Occupancy. In this study, we present the mathematical formulas, algorithm, and numerical illustration of the MF-CTM for a one-lane single road link. By simulation using MF-CTM, we demonstrate that the model can: i) represent decreasing queue length and dissipation time during congestion as CAV share increases, ii) maintain the order of vehicle groups (traffic cohort) during congested condition, iii) represent fluctuation in link outflow rates due to fluctuating CAV shares in the traffic. For further studies, the formulations and algorithm of MF-CTM can be extended into networks and include more elements e.g., signalised intersections, bus lanes, etc. In the future, we expect that the MF-CTM can be a useful tool to assess the effectiveness of traffic management measures involving CAVs.

Integrating land use and transportation systems, notably through the Transit-Oriented Development (TOD) paradigm, is increasingly vital in urban metropolitan regions. Park-and-Ride (P&R) facilities are integral parts of TOD, facilitating seamless transition from private vehicles to public transit, which through to mitigate traffic congestion in urban areas. Employing a quantitative data analysis that combines primary surveys and secondary data collection, this study assesses the performance of TOD stations in Greater Jakarta (GJ), with a specific focus on the impact of formal and informal P&R provisions. A node-place model is developed, that encompasses both formal and informal P&R spaces, to analyse the relationships between land use activities and transportation accessibility at TOD stations. The findings reveal that P&R facilities, particularly informal motorcycle parking, positively influence transit ridership. Integrating P&R improves transit accessibility, particularly for motorcycle users facing limited public transport coverage. The study highlights the role of P&R facilities in supporting sustainable urban mobility and identifies key factors influencing TOD performance. Based on modelling and data analysis, the study proposes policy interventions, including optimizing land use, enhancing pedestrian networks, and integrating transportation accessibility, with a particular focus on effectively managing both formal and informal P&R facilities for cars and motorcycles.

Alternative intersection designs can provide cost-effective solutions to overcome the proven inadequacy of conventional approaches. Several studies have assessed the performance of alternative designs against a range of traffic volumes and geometric design aspects, each in isolation, but a model which can factor in multiple variables into the analysis is the identified research gap. The displaced left-turn - DLT intersection design was found to be the most versatile, efficient, and transferable to locations elsewhere in the world. In this paper, a displaced right-turn intersection - a variant of DLT, was modelled for a range of traffic flows and design conditions. Regression models were developed for Practical Reserve Capacity and Delay as dependent variables with traffic flow, proportion of right-turning traffic, signal cycle time and length of displaced turn as explanatory variables. These models can provide relatively quick preliminary estimates of the performance indicators before committing to resource-consuming junction remodelling works.

This research is aimed at developing a method for relocating wholesale markets in a city with the objective of decongesting the central area by improving the traffic efficiency and to make it pollution free. This paper proposes a bi-level optimisation framework pursuing the local authority’s objective of maximising welfare benefits relative to the spend ensuring good value for money at the upper level. The lower-level framework considers retailers’ response to the relocation of wholesale markets allowing them the choice of procurement location. The lower-level problem also models the route choice of commercial vehicle traffic as well as the private vehicle traffic to measure the resulting on-street congestion. The bi-level problem has been solved with integer Particle Swarm Optimisation algorithm for the case of Bandung, Indonesia. The results show that relocating wholesale markets improves the city centre traffic efficiency and pollution level by about 14%. Traffic speeds over the entire city also improve by up to 6.6% and the pollution levels marginally would drop too. Market relocation as a strategy would significantly improve the efficiency and pollution levels but must be carefully planned and evaluated otherwise the emissions outside of city centre could increase.

While extensive research has been conducted to explore factors influencing mode choices and first- or last-mile connectivity, few studies have delved into the underlying hierarchy of decision making processes. An understanding of this hierarchy, which illustrates causal relationships, is crucial for modeling travel decisions, as trip structure depends on choice behavior and vice versa. Traditional mode choice models often neglect these underlying causal relationships, necessitating the development of new models. By incorporating mediating effects of trip chaining and mode choice, alongside traditional factors, a more holistic understanding of mode choice behavior is achieved. In this study, hierarchical relationships are identified between trip chaining and mode choice in Hyderabad, India, using a structural equation modeling (SEM) method, owing to its inherent strength in handling latent causal relationships. SEM analysis provides the total effects of sociodemographic variables on mode choices and trip chain types through these causal relationships. Findings reveal that, for nonwork trips, the decision making process is simultaneous, regardless of the mode chosen. In contrast, for work trips, the decision making process is simultaneous for active and public modes, but the choice of mode precedes trip chaining for private modes. Furthermore, in this study, we learn from those who own private vehicles but use active or public transport by choice and extract the factors that had indeed motivated their choice. Confirmatory factor analysis is employed to validate the identified factors. The identified factors, coupled with the understanding of decision making hierarchy, offer valuable insights for shaping policies that can maximize the potential of active and public transport modes.

Due to an alarming threat of air pollution and climate change, governments around the world are now actively promoting electric vehicles. The case for vehicle electrification is even more important in big cities of developing countries, where motorcycle is a dominant mode of travel. To promote electric motorcycles successfully, we need to understand the factors that would drive the consumer choices when buying a motorcycle. This study chose Bandung in Indonesia as the case study location, where nearly 75% of vehicles are motorcycles. This study conducted a survey of preferences from over 700 residents and included battery charging methods such as plug-in/battery swap at home/office, superfast charging at stations, and deployed an innovative modelling approach constraining the mixture of distributions for monetary attributes. The study found that quick recharge in 10 minutes and battery swap at station are preferred over the base method of plug-in at home/work. The battery swap at home has been perceived the same as plug-in home/work and the respondents are indifferent to this option.

This paper presents new algorithms for restoring seaport operations after a disaster and develops a model considering interdependencies to select an efficient course of action. The model prioritises the infrastructure to be repaired, identifies the equipment required and the number of repair teams to be deployed. This paper de-velops a new dynamic programming model to assign multicrew repair teams and shows that the solution is exact. This paper then develops a new variant of the Hungarian Algorithm by embedding an exploitation-exploration strategy to obtain an approximate solution for large-sized assignment problems. Furthermore, this paper solves the restoration problem in totality by accounting for interdependencies between marine/land-side infrastruc-ture/equipment and repair team assignments. This paper also develops a new variant of Genetic Algorithm based on a deletion-mutation technique and explores reducing the computation time involved in solving optimisation problems. This paper applies the principles laid out to restore Pantoloan seaport in Indonesia which was struck by a tsunami. The approximate solution obtained by the extended Hungarian Algorithm for small problems is quicker and matches with the exact solution obtained by the new dynamic programming. In case of large-sized problems, the extended Hungarian Algorithm has been found to arrive at a solution which allows reopening the seaport 48 % sooner than the other algorithms. The new variant of Genetic Algorithm outperforms the Genetic Algorithm with Local Search, needing only 40 % of the computation time and the solution found to be particularly stable too.

The development of a multimodal transportation network to improve traffic efficiency is gaining extensive interest from researchers. Shifting road traffic to other modes of transport potentially saves travel-related costs. In particular, shifting freight traffic away from roads may reduce the deterioration of pavements, which influences the decisions related to pavement maintenance, rehabilitation and reconstruction requirements. However, there is little evidence on the impact of the shifting phenomenon on road maintenance requirements. This article presents a new highways agency-focused model that integrates pavement maintenance, rehabilitation and reconstruction decisions with development of a multimodal transportation network involving railway and seaway routes. The model is presented within an optimization framework and illustrates the application to a real-life case study. A greedy heuristic is modified by incorporating a threshold-based strategy, aligning the model with the highways agencies’ workflow, bringing with it the benefits offered by the optimization.

Trip makers rely more on personalised vehicles (2W/cars) for trip chaining due to their flexibility, rather than using environmentally efficient public transport. More concerning is the role of walking/cycling and they could potentially contribute to sustainability and improve access for all if integrated well with public transport. The uniqueness of this study is that active modes are considered as part of trip chains and hypothesises that the uptake of active modes will increase if integrated into the trip chains instead of considering them in isolation. This study firstly explores the hierarchical relationship between trip chaining and mode choices for active/public modes and private transport using SEM. The driving factors are identified from data set of those who own private vehicles but are using active/public transport by choice. Identified factors could be used to address the barriers for private transport users to uptake active modes as a part of trip chaining.

Traditional markets play a key role in local supply chains in many countries, often influencing retailer decisions due to their inherent attractiveness. In contrast to restocking choices for retailers as part of large chains, choices of independent retailers driven by local traditional markets have not been widely researched and are not well understood. This paper analyses the factors influencing independent retailer restocking choices and investigates the interplay between the presence of traditional markets and retailer choices. Bandung city in Indonesia is chosen for the study where independent retailers are prevalent, and where a number of traditional markets are thriving. A retrospective questionnaire was used to capture independent retailer restocking behaviour and generation models were calibrated to arrive at the trip propensity. Discrete choice models were estimated to explain the retailer preferences for supplier location and transport service choice. Results indicate that trips generated by independent retailers are explained by the presence of traditional markets and retailers’ vehicle ownership, in addition to the standard variables such as number of persons employed, weekly goods demand and average shipment weight. As for restocking location choice, retailers are more likely to choose suppliers within a traditional market where the number of wholesaler units is larger. Furthermore, the choice of traditional markets has a positive influence on whether retailers choose to use their own vehicle to restock their shops.

This research investigates how saturation flow is affected by bus stops and analyses whether the standard equation used in the UK is adequate for estimating the saturation flow of an approach, especially in the presence of a downstream-side bus stop. As part of the study, we undertook a survey of saturation flows at several junctions in the city of Leeds in England and seek to explain the factors affecting them, taking into account the bus stop located nearby. We develop bootstrapping regression models to explain the difference between the observed and estimated saturation flows and propose an extension to the standard model, accounting for the bus stop located nearby. Finally, this paper illustrates the methods developed and reports on how performance can be improved by reconfiguring a junction.

Historically people traded the risk of living in dangerous places such as volcanic slopes for the benefit of farming in rich soils. Road network around risk prone area plays a key role in saving lives when evacuation is required in an emergency, and thus needs to be in full preparedness to face the eventuality. This will need analysing vulnerability to disruption and identifying critical network links of the evacuation routes. It is also crucial to ensure that the evacuees are aware of recommended routes to sheltered areas. Traffic models to assess road network performance due to natural disasters have been developed in the past. But few researchers investigated whether the evacuees are aware of the recommended routes to sheltered areas and whether they are willing to use them indeed. This paper adopts a vulnerability index and identifies network links to improve, by mapping them to a simple 'traffic light style' congestion scale. A special mobile phone application software was developed to guide the residents to reach sheltered areas which takes account of the fact that a third of the residents living around Mt Merapi are not aware of evacuation routes to sheltered areas.

In recent years, climate change emerged as a dominant concern to many parts of the world bringing in huge economic losses disturbing normal business/life. In particular cities are suffering from floods affecting land based transportation systems in a significant manner more frequently than ever. Many local authorities facing funding cuts are suffering from limited budgets and they are put under even higher pressure when looking for resources to recover the damaged networks. The agencies involved with post-disaster reconstruction too struggle to prioritise the network links to recover. This paper addresses the problem of road maintenance/development with the aim of improving resilience of the network by formulating the problem as a mathematical model that minimises the vulnerability to disruption due to natural incidents under budgetary constraints. This paper extends the critical link analysis from a single link being disrupted to the case of multiple links, and for the first time proposes an objective function involving a measure of vulnerability to minimise. Metaheuristic Simulated Annealing method is used to reach near global optimal solution for a real-life network with large demand. A segment of the City of York in England has been used to illustrate the principles involved. Numerical experiments indicate that Simulated Annealing based optimisation method outperforms the 'volume-priority' heuristic approach, returning higher value for money spent. The proposed approach spreads the benefits across wider population by including more number of links in the priority list while reducing the vulnerability to disruption.

Traffic congestion has been a major problem in big cities around the world, not to mention several large cities in Indonesia. Bandung is the second largest metropolitan area after Jakarta in Indonesia which suffers from extreme levels of congestion. With a high number of motorcycles and large private car population, congestion in this city is ever growing worsening the environment. While the local authorities struggle to find resources to fund capital intensive capacity expansion projects, this research explores the use of cost effective demand management policy measures to reduce the congestion and pollution. This study aims at assessing two relatively under-researched demand management policy measures that restrict vehicle flows viz., car-free day and odd-even plate schemes to investigate the effect on traffic congestion and the environment. SATURN traffic network modelling software has been used to predict the route choices of vehicles. Bandung city road network and origin destination matrix have been adapted to simulate the two measures during the peak hour. As well as providing the necessary inputs to a pollutant emission estimation model, traffic network modelling output forms the basis for assessing the congestion levels. Results show that both car-free day and odd-even plate measures have unintended consequences that undermine their effectiveness which if addressed could make them highly beneficial solutions. Car-free day scheme reduces the traffic flow levels in the vicinity of scheme but diverts the vehicle flow elsewhere to other routes which may adversely affect the congestion/pollution. Odd-even plate scheme is very effective at the beginning of its implementation but the performance gradually diminishes as drivers start to adapt by buying a second vehicle or even using fake number plates.

Roadworks are perhaps the most controversial topic in transport professional field. On one hand, they are a necessity to assure the current and future functionality of the traffic network, while on the other, they are seen as a major disturbance by road users with concerns for excessive travel time delays. The impact of roadworks is usually analysed at a local level however the network-wide effects are crucial to ensure reliable travel times. Moreover the analysis usually focusses on private cars and the reliability impact on public transport services are too important to ignore. This paper investigates the impact of roadworks undertaken on a given road link over wider parts of the network and assesses travel time reliability for both cars and buses. This research involves setting up of a conventional network assignment model to arrive at the route choice of drivers as a result of the roadworks and then integrates the outcomes with a microsimulation model to generate space-time trajectories to arrive at travel times of individual vehicles. We adopted a reliability measure from the literature to compute travel time reliability of a given type of vehicle by unique origin-destination (O-D) pair combinations and also more generally to provide a wider picture at an aggregated network level. The method was tested on a real life network in England, and travel time reliability results were analysed both at the network scale and significant O-D pair level for private cars and bus routes.

This paper analyses suburban rail fare elasticity and compares the results across five suburban divisional operations of the Indian Railways in three cities viz., Chennai, Kolkata and Mumbai. The three cities chosen have a highly varying modal share of public transport trips and thus offer interesting insights into the attitudes of trip makers towards the changes in operational variables such as fares, service levels. This paper contributes towards understanding of the determinants of demand for public transport in a developing country and applies econometric methods involving static and dynamic modelling methodologies. This research addresses the question of smaller sample sizes which constrain the use of standard regression approaches and applies a bootstrapping method which substitutes for traditional assumptions on distributions and asymptotic results. It was found that the suburban rail demand is inelastic to fare which indicates that the revenue would increase with an increase in fare. Finally, the paper illustrates the use of computed elasticities by estimating the demand for suburban rail in Kolkata.

The aim of this paper is to model the impacts of competition between cities on both the optimal welfare generating tolls and upon longer-term decisions such as business and residential location choices. The research uses a dynamic land use transport interaction model of two neighbouring cities and analyses the impacts by setting up a game between the two cities to maximise the welfare of their own residents. The work builds on our earlier research which studied competition in a small network using a static equilibrium approach for private car traffic without accounting for the land use responses to the change in accessibility. This paper extends the earlier work by setting up a dynamic model which includes active modes of travel and the more usual car and public transport in a realistic twin city setting and assesses the longer term relocation responses. This paper firstly sets out the competition between two hypothetical identical cities i.e. the symmetric case; and then sets out the real world asymmetric case in which the cities are of different size representative of Leeds and Bradford in the UK but equally applicable elsewhere too. We found that the level of interaction between the two cities is a key determinant to the optimal tolls and welfare gains. Our findings show that the competition between cities could lead to a Nash Trap at which both cities are worse off in terms of welfare gains. On the other hand, we found that cities, if regulated, would gain in terms of welfare and yet charge only half the toll compared with tolls under competition. We then show that the effect of competition increases with increased interaction between cities. In terms of residential location, cities with higher charges benefit from an increase in residents, though as with other studies, the relative change in population in response to cordon charging is small. The policy implications are threefold—(i) while there is an incentive to cooperate at local authority level, this is not achieved due to competition; (ii) where cities compete they may fall into a Nash Trap where both cities will be worse off compared to the regulated solution; and (iii) regulation is recommended when there is a strong interaction between the cities but that the benefits of regulation decrease as interaction between cities decreases and the impact of competition is lessened.

In this paper we model the competition between two cities as a game to maximise the welfare considering the impact of demand management strategies in the form of cordon tolls. This research builds on earlier work which studied the competition in a small tolled network meant for private modes of transport which have a choice of route. The earlier work showed that while both cities have an incentive to charge alone, once they begin, they are likely to fall into the ‘Nash Trap’ of a prisoner's dilemma where the incentive to defect is higher than that to cooperate thus eventually leading to a ‘lose–lose’ situation. The current paper extends the idea of competition between cities by setting up a system dynamic model of two cities which includes modes such as car, bus, train and walking and cycling. This paper innovates by integrating the simulation of land use transport interactions with a class room style experimental game and analyses the gaming strategies from a continuous repeated prisoner's dilemma involving setting of tolls to maximise the welfare of residents. The aim is to test (a) whether the strategies adopted are as theory predicts and (b) whether the players recognise the benefits of lower tolls when given information about the regulated solution and collaborate or continue to play to win. The results show that players respond to the information and maintain a collaborative solution which may have significant implications for regulation and the development of cities within regional partnerships.

Large off-street car parks are traditionally modelled as self-standing traffic zones representing origins/destinations in standard network assignment models. However, such a treatment precludes the drivers from choosing alternative car parks as it assumes the car parks are their final destinations. This paper discusses the feasibility of bringing car park choice and the effects of capacity within a traditional network assignment model. The search time within car parks depends on the car park occupancy and can be represented by a flow-delay type function on the car park occupancy/ capacity. This research calibrates the search-time function based on practically observed occupancy and search time at two city-centre car parks in Leeds, England. The analysis follows a simple fixed search-time method as well as a sophisticated variable search-time method. The results are validated against the observed occupancies at the car parks. A car park specific constant was introduced to account for the unobserved preference for a given type of parking facility. In a multiperiod assignment, when car park occupancies are passed on dynamically, both fixed and variable search-time approaches are seen as an improvement over the standard approach, with the variable search time outperforming the fixed time approach.