Elasticities of demand for leisure air travel: A system modelling approach

In this study, the UK outbound demand for leisure air travel has been examined using a demand system which takes into account the ways in which the expenditure on air fares interacts with both the expenditure on non-fare components of travel abroad and with expenditure on domestic leisure. The findings suggest that there is a strong link between total consumer expenditure and expenditure on air travel with the expenditure elasticity greater than one but that, considered in aggregate, the demand for air travel is moderately inelastic with respect to air fares alone. However, the findings also show that interactions between air travel expenditure, other costs of travel abroad, as well as expenditure on leisure activities in the UK, involve sizeable cross-price elasticities.     

A model for taxi pooling with stochastic vehicle travel times

Taxi pooling means the sharing of a taxi by more than one passenger with at least a semi-common route. Currently, a trial-and-error process is adopted, taking into account expected travel times, for taxi pooling, a method which is neither effective nor efficient. This is because stochastic disturbances arising from variations in the taxi travel times that occur in actual traveling are neglected. In the worst case scenario, where vehicle travel times fluctuate wildly during operations, the planned schedule can be disturbed enough to lose its optimality. Therefore, in this study, a network flow technique is used to construct a stochastic taxi pooling model incorporating stochastic vehicle travel times. A solution algorithm is also proposed to efficiently solve the problem. The performance of the proposed models and the use of the solution method in practice are evaluated by executing numerical tests using real data with suitable assumptions. The test results show that the stochastic model and the solution method could be useful references for practice.     

A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows

For VRP with time windows (VRPTW) solved by conventional cluster-first and route-second approach, temporal information is usually considered with vehicle routing but ignored in the process of clustering. We propose an alternative approach based on spatiotemporal partitioning to solving a large-scale VRPTW, considering jointly the temporal and spatial information for vehicle routing. A spatiotemporal representation for the VRPTW is presented that measures the spatiotemporal distance between two customers. The resulting formulation is then solved by a genetic algorithm developed for k-medoid clustering of large-scale customers based on the spatiotemporal distance. The proposed approach showed promise in handling large scale networks.     

A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty

This paper addresses an integrated model that schedules multi-item replenishment with uncertain demand to determine delivery routes and truck loads, where the actual replenishment quantity only becomes known upon arrival at a demand location. This paper departs from the conventional ant colony optimization (ACO) algorithm, which minimizes total travel length, and incorporates the attraction of pheromone values that indicate the stockout costs on nodes. The contributions of the paper to the literature are made both in terms of modeling this combined multi-item inventory management with the vehicle-routing problem and in introducing a modified ACO for the inventory routing problem.     

An exact solution approach for vehicle routing and scheduling problems with soft time windows

A new column generation based exact optimization approach for the vehicle routing and scheduling problem with semi soft time windows (VRPSSTW) is presented. Elementary shortest path problem with resource constraints and late arrival penalties is solved as a subproblem, which rises from the Dantzig–Wolfe decomposition method. Exact solutions of VRPSSTW and hard time windows variant are compared on Solomon’s benchmark instances as well as on an instance based on Tokyo road network. It was found that the VRPSSTW solution results in fewer routes thus overall costs are reduced and late arrival penalties contribute only a small fraction to total cost.     

Understanding the travel behaviors and activity patterns of the vulnerable population using smart card data: An activity space-based approach

Understanding the travel behaviors and activity patterns of vulnerable people is important for addressing social equity in urban and transportation planning. With the increasing availability of large-scale individual tracking data, new opportunities have emerged for studying people's travel behaviors and activity patterns. However, the data has not been fully exploited to examine the travel characteristics of vulnerable people and their implications for understanding transport-related disadvantage. This study proposes a methodological framework based on the concept of activity space that enables a comprehensive examination of vulnerable people's spatiotemporal travel characteristics and an investigation of the geographies of transport disadvantage. Using the proposed framework, a case study that investigates the bus activities of the vulnerable population using four-month smart card data is carried out in the city of Wuhu, China. The case study suggests that vulnerable people possess distinct travel behaviors that differ considerably from the mainstream population and that the implications of transport disadvantage, as revealed by the participation in bus activities, vary across different demographic groups and across different spatial contexts. Some of the empirical insights obtained from this study also differ from conclusions drawn from previous studies and will enrich our understandings of vulnerable people's activities. Overall, the paper makes two major contributions. Methodologically, the proposed framework can overcome some of the deficiencies of activity space-based approaches for understanding transport disadvantage and contribute broadly to the studies of travel behaviors and activities patterns using individual-level tracking data. Empirically, the study identifies varying spatial and temporal implications of transport disadvantage associated with different vulnerable groups, which could further shed light on public transit planning and service design.     

A heuristic approach for the green vehicle routing problem with multiple technologies and partial recharges

This paper presents several heuristics for a variation of the vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need for recharge during their duties. In addition to the routing plan, the amount of energy recharged and the technology used must also be determined. Constructive and local search heuristics are proposed, which are exploited within a non deterministic Simulated Annealing framework. Extensive computational results on varying instances are reported, evaluating the performance of the proposed algorithms and analyzing the distinctive elements of the problem (size, geographical configuration, recharge stations, autonomy, technologies, etc.).     

A multi-start heuristic approach for the split-delivery vehicle routing problem with minimum delivery amounts

We propose a new multi-start solution approach for the split-delivery vehicle routing problem with minimum delivery amounts (SDVRP-MDA). Initial solutions are generated by both node-insertion and route-addition procedures with a single parameter to control the restart. These solutions are then improved by a variable neighborhood descent metaheuristic with a novel search operator inspired by node-ejection chains. We test the proposed approach with 32 benchmark instances for four different minimum delivery fractions. Using the proposed algorithm, out of 128 cases tested, we find 81 best known solutions and 34 new best solutions; overall, we find 43 new best solutions.     

Analysis of the potential demand for battery electric vehicle sharing: Mode share and spatiotemporal distribution

Carsharing is considered one of the solutions to urban transport problems. As a new mode in the urban transport system in China, there are still initial questions of how carsharing will perform and what the impacts will be. Accordingly, this study considers battery electric vehicle sharing and investigates its potential demand, with Beijing as the case study. A nested logit model is established and calibrated to analyze mode choice behavior. Further, real trip data is used to estimate the potential demand for battery electric vehicle sharing. In addition, the temporal and spatial distribution of potential demand, the impact of battery electric vehicle sharing on the mode split, and the impact of pricing strategies are analyzed. The results show that an optimistic mode split of battery electric vehicle sharing is 4.23% when the average distance between travelers and stations is 0.5 km. The main source of potential demand is public transport. However, the substitution effect of battery electric vehicle sharing for private vehicles is weak. The potential trips are concentrated in the morning peak period, mainly starting in residential or integrative areas, and ending in commercial areas or green spaces. Commuting and long-distance trips are more sensitive to decreases in price, such that they are more likely to be completed as battery electric vehicle sharing trips. This price decrease could also increase the potential trip ratio during the evening peak period. These findings are useful to governments and operators for implementing policies such as station planning, relocation, and pricing strategies.     

Control-based optimization approach for aircraft scheduling in a terminal area with alternative arrival routes

This paper presents an optimization approach for dynamically scheduling aircraft operations and supporting air traffic controllers in both determining and implementing operationally feasible landing and departure times at an airport. The mixed integer linear programming model proposed incorporates air traffic control infrastructure in terms of route network, introduces the concept of alternative approach routes and is designed to generate an output that can be converted into effective advisories for executable flight commands. It shows reasonable computational times for obtaining the optimal solution and delay reductions of up to 35% with practical size instances from Sao Paulo/Guarulhos International Airport.     

Non-linear associations between built environment and active travel for working and shopping: An extreme gradient boosting approach

Active travel has environmental, social, and public health-related benefits. Researchers from diverse domains have extensively studied built-environment associations with active travel. However, limited attention has been paid to distinguishing the associations between built environment characteristics at both the origins and destinations and active travel for working and shopping. Scholars have started to examine non-linear associations of built environment with travel behaviour, but active travel has seldom been a focus. Therefore, this study, selecting Xiamen, China, as the case, utilises a state-of-the-art machine learning method (i.e., extreme gradient boosting) to explore the non-linear associations between built environment and active travel for working and shopping. Our findings are as follows. (1) For both purposes, trip characteristics contribute the greatest, and the built environment is also quite important and has larger collective contributions for active travel than does socioeconomics. (2) The relative importance of built environment on active travel for shopping is evidently larger than that for working. (3) All built-environment variables have non-linear associations with active travel, and associations with active travel for working are generally in inverted U or V shapes, while those with shopping trips have much more complex patterns. (4) Differences in the threshold value and gradient exist between built-environment associations with active travel for working and shopping and between variables at origins and destinations. Decision makers are recommended to meticulously disentangle the complex influences of built environment on active travel and distinguish between diverse purposes to make informed and targeted interventions.     

Airline strategies for aircraft size and airline frequency with changing demand and competition: A simultaneous-equations approach for traffic on the north Atlantic

Airport planners need to know the forecast demand on the facilities provided airside at airports. For this they need to know how airlines will deal with traffic in terms of the size of aircraft and frequency of service. In response to increasing demand, airlines may increase capacity by increasing the frequency of flights or they may choose to increase aircraft size. This may yield operating cost economies. If the airports they operate from are capacity constrained they will be limited in the extent that they can change frequency that will limit their ability to compete with the number of frequencies offered. Consequently, these airports are excluded as are major hubs as frequencies will be influenced by connecting passengers. Routes are identified on the north Atlantic that can be analysed and conclusions are suggested on the basis of three stage least-squares estimates for pooled time series-cross section data. An increase in passengers on the whole will result in a larger increase in frequency than in aircraft size but the impact of competition does not yield significant results due to the strategy of excluding certain categories of airport.     

Investigating the inter-departmental synergy in demand for rooms and auxiliary services: a spatial econometric approach

This study investigates interdependence among hotel revenue centres, using dataset on room guests and customers of auxiliary departments in Seoul, Korea. Spatial econometric methods are applied to examine the causal relationships between demand for rooms and auxiliary facilities. Results suggest that while the room demand significantly contributes to demand for auxiliary services, the reverse does not hold.     

A robust optimization approach for the road network daily maintenance routing problem with uncertain service time

This paper studies the robust optimization approach for the routing problem encountered in daily maintenance operations of a road network. The uncertainty of service time is considered. The robust optimization approach yields routes that minimize total cost while being less sensitive to substantial deviations of service times. A robust optimization model is developed and solved by the branch-and-cut method. In computational experiments, the behavior of the robust solutions and their performance are analyzed using Monte Carlo simulation. The robust optimization model is also compared with a classic chance-constrained programming model. The experimental analysis provides managerial insights for decision makers to determine an appropriate routing strategy.     

Carrying capacity procurement of rail and shipping services for automobile delivery with uncertain demand

The determination of the optimal carrying capacity procurement of rail and shipping services in the automobile intermodal network with unique characteristics is essential to save automobile delivery cost. In this research we develop a two-stage stochastic programming model for the tactical-level decision problem arising in the special automobile intermodal network. Furthermore, we improve the sample average approximation algorithmic procedure to solve the model. We apply the model and solution method to a case study associated with the Shanghai Automobile Industry Corporation. We believe that this study deals with an emerging new research topic with practical significance for the automobile industry.     

Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads

This paper addresses the routing problem with unpaired pickup and delivery with split loads. An interesting factor of our problem is that the quantity and place for pickup and delivery are decision variables in the network. We develop an easy-to-implement heuristic in order to gain an efficient and feasible solution quickly. Then, a local search algorithm based on the variable neighborhood search (VNS) method is developed to improve the performance of the heuristic. Computational results show that the proposed VNS method is able to obtain an optimal or near optimal solution in reasonable time for the formulated problem.     

Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost

The vehicle routing problem (VRP) with stochastic demands and weight-related cost is an extension of the VRP. Although some researchers have studied the VRP with either stochastic demands or weight-related cost, the literature on this problem is quite limited. We adopt the a priori optimization to tackle this problem and propose a dynamic programming to compute the expected cost of each route. We develop the adaptive large neighborhood search heuristics equipped with several approximate methods for the problem. To evaluate our heuristics, we generate 84 test instances. Computational results demonstrate the performance of our heuristics and can serve as benchmarks for future researchers.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

Vehicle Routing Problems (VRPs) in distribution centers with cross-docking operations are more complex than the traditional ones. This paper attempts to address the VRP of distribution centers with multiple cross-docks for processing multiple products. In this paper, the mathematical model intends to minimize the total cost of operations subjected to a set of constraints. Due to high complexity of model, it is solved by using a variant of Particle Swarm Optimization (PSO) with a Self-Learning strategy, namely SLPSO. To validate the effectiveness of SLPSO approach, benchmark problems in the literature and test problems are solved by SLPSO.