Short-term shift setting and manpower supplying under stochastic demands for air cargo terminals

A good air cargo terminal manpower supply plan helps terminals deal efficiently with their cargos and reduces their operating costs. To design a good air cargo terminal manpower supply plan, a terminal has to consider not only its operating costs, but also the uncertainty of the manpower demand in actual operations. However, most air cargo terminals in Taiwan currently depend on staff experience with a fixed demand when establishing the manpower supply plan, which is neither effective nor efficient. We have developed two stochastic-demand manpower supply plan models for air cargo terminals that can resolve stochastic demands occurring in practice. The objectives of both models are to minimize the total man-hour cost, subject to the related operating constraints. The models are formulated as integer/mixed integer linear programs. To evaluate the two stochastic-demand models under stochastic demands, we have also developed two deterministic-demand manpower supply plan models, by suitably modifying two stochastic-demand models, respectively, and an evaluation method. Here, we perform a case study using real operating data from a Taiwan air cargo terminal. The preliminary results are good, showing that the models could be useful for planning air cargo terminal manpower supply.

Optimal deployment of charging lanes for electric vehicles in transportation networks

Given the rapid development of charging-while-driving technology, we envision that charging lanes for electric vehicles can be deployed in regional or even urban road networks in the future and thus attempt to optimize their deployment in this paper. We first develop a new user equilibrium model to describe the equilibrium flow distribution across a road network where charging lanes are deployed. Drivers of electric vehicles, when traveling between their origins and destinations, are assumed to select routes and decide battery recharging plans to minimize their trip times while ensuring to complete their trips without running out of charge. The battery recharging plan will dictate which charging lane to use, how long to charge and at what speed to operate an electric vehicle. The speed will affect the amount of energy recharged as well as travel time. With the established user equilibrium conditions, we further formulate the deployment of charging lanes as a mathematical program with complementarity constraints. Both the network equilibrium and design models are solved by effective solution algorithms and demonstrated with numerical examples.     

Comprehensive pavement maintenance strategies for road networks through optimal allocation of resources

In order to maintain a growing road infrastructure at some minimum level of service, substantial resources are required on a recurrent basis. Of late, the available resources can no longer meet all the maintenance and rehabilitation demand even in wealthy nations. Hence, there is a need to develop a tool which will optimally allocate these resources in order to keep the road infrastructure as ‘healthy’ as possible. Further, this tool must acknowledge that maintenance needs are not only restricted to structural aspects but also extend to the functional- and safety-related aspects of a road. Here, such a comprehensive optimization tool is developed which when used will optimally allocate resources in order to maintain a healthy (from structural, functional, and safety standpoints) road network. The problem of determining the optimum maintenance and rehabilitation activities for individual road sections is formulated as a linear integer programming problem. Results from a case study using the proposed method show that the suggested maintenance and rehabilitation plans make sense from engineering and economic considerations.     

Model and a solution algorithm for the dynamic resource allocation problem for large-scale transportation network evacuation

Allocating movable resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic resource allocation problem for transportation evacuation planning on large-scale networks. The proposed model is built on the earliest arrival flow formulation that significantly reduces problem size. A set of binary variables, specifically, the beginning and the ending time of resource allocation at a location, enable a strong formulation with tight constraints. A solution algorithm is developed to solve for an optimal solution on large-scale network applications by adopting Benders decomposition. In this algorithm, the MILP model is decomposed into two sub-problems. The first sub-problem, called the restricted master problem, identifies a feasible dynamic resource allocation plan. The second sub-problem, called the auxiliary problem, models dynamic traffic assignment in the evacuation network given a resource allocation plan. A numerical study is performed on the Dallas–Fort Worth network. The results show that the Benders decomposition algorithm can solve an optimal solution efficiently on a large-scale network.     

Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization

Rapid motor vehicle crash detection and characterization is possible through the use of Intelligent Transportation Systems (ITS) and sensors are an integral part of any ITS system. The major focus of this paper is on developing optimal placement of accident detecting omnidirectional sensors to maximize incident detection capabilities and provide ample opportunities for data fusion and crash characterization. Both omnidirectional sensors (placed in suitable infrastructure locations) and mobile sensors are part of our analysis. The surrogates used are acoustic sensors (omnidirectional) and Advanced Automated Crash Notification (AACN) sensors (mobile). This data fusion rich placement is achieved through a hybrid optimization model comprising of an explicit–implicit coverage model followed by an evaluation and local search optimization using simulation. The compound explicit–implicit model delivers good initial solutions and improves the detection and data fusion capabilities compared to the explicit model alone. The results of the studies conducted quantify the use of a data fusion capable environment in crash detection scenarios, and the simulation tool developed helps a decision maker evaluate sensor placement strategy.     

The integration of Taiwanese and Chinese air networks for direct air cargo services

The two sides of the Taiwan Strait perform mutually dependent but complementary activities in the global manufacturing supply-chain. As a result, trade between Taiwan and China grew in double digits annually in the 1990s. With growing economic ties, direct air links are inevitable. In this research, we analyzed government documents and interviewed the air cargo carriers and airlines that currently serve the Taiwan–China air cargo market. This information enabled us to tabulate the trade, estimate the airport-to-airport air cargo demand and calibrate the international and domestic freight tariffs. We used a connectivity measurement and classified Chinese airports into national, regional and local classes in a hub-and-spoke air cargo network. We developed a mathematical model and a branch-and-bound algorithm. The results showed that at least two transit airports are economically necessary for a Taiwan–China air link. Shanghai and Xiamen were always the top two transit airports. The third airport would be Changsha if the decision becomes three air-links. These links are different from the top three passenger transit airports, Fuzhou, Xiamen and Shanghai, even though the cost saving is moderate.     

A discrete choice framework for modeling and forecasting the adoption and diffusion of new transportation services

Major technological and infrastructural changes over the next decades, such as the introduction of autonomous vehicles, implementation of mileage-based fees, carsharing and ridesharing are expected to have a profound impact on lifestyles and travel behavior. Current travel demand models are unable to predict long-range trends in travel behavior as they do not entail a mechanism that projects membership and market share of new modes of transport (Uber, Lyft, etc.). We propose integrating discrete choice and technology adoption models to address the aforementioned issue. In order to do so, we build on the formulation of discrete mixture models and specifically Latent Class Choice Models (LCCMs), which were integrated with a network effect model. The network effect model quantifies the impact of the spatial/network effect of the new technology on the utility of adoption. We adopted a confirmatory approach to estimating our dynamic LCCM based on findings from the technology diffusion literature that focus on defining two distinct types of adopters: innovator/early adopters and imitators. LCCMs allow for heterogeneity in the utility of adoption for the various market segments i.e. innovators/early adopters, imitators and non-adopters. We make use of revealed preference (RP) time series data from a one-way carsharing system in a major city in the United States to estimate model parameters. The data entails a complete set of member enrollment for the carsharing service for a time period of 2.5 years after being launched. Consistent with the technology diffusion literature, our model identifies three latent classes whose utility of adoption have a well-defined set of preferences that are significant and behaviorally consistent. The technology adoption model predicts the probability that a certain individual will adopt the service at a certain time period, and is explained by social influences, network effect, socio-demographics and level-of-service attributes. Finally, the model was calibrated and then used to forecast adoption of the carsharing system for potential investment strategy scenarios. A couple of takeaways from the adoption forecasts were: (1) placing a new station/pod for the carsharing system outside a major technology firm induces the highest expected increase in the monthly number of adopters; and (2) no significant difference in the expected number of monthly adopters for the downtown region will exist between having a station or on-street parking.     

Application of optimal subset selection to problems of design and scheduling in urban transportation networks

The paper describes some possibilities for modifying the optimal network algorithm developed by Boyce, Farhi and Weischedel in a way that makes it applicable to some practical problems of network planning. The modifications, which have been tested with respect to their effect on the efficiency of the algorithm, include the introduction of asymmetrical demand structures, the integration of an existing network, the lexico-minimization of a dynamic objective function, and the consideration of constraints related to interdependencies between candidate links. Two small network problems and one medium-sized problem (61 nodes, 104 links, 16 candidates) have been computed; the results support the hypothesis that the algorithm may be applied to produce approximate solutions to problems of practical dimensions within a reasonable range of time.     

Stationary ultracapacitors storage device for improving energy saving and voltage profile of light transportation networks

The installation of stationary ultracapacitor storage devices, as widely recognized, allows the recovery of the braking energy for increasing the energy efficiency as well as a better pantograph voltage profile. In the paper a new methodological mean is proposed for determining the fundamental characteristics of this kind of storage device, characterized by high power density, interfaced with the railroad by a bidirectional dc-dc converter. More specifically, the parameters of the storage system can be determined by employing an optimization technique which in a quite general way is able to take contemporaneously into account several aspects in an integrated manner. Some considerations are performed for properly taking into account the stochastic aspects of the design procedure. Numerical simulations with respect to a case study are presented, pointing out the potentiality of the tailored technique. Experimental results are also reported, with reference to an electromechanical simulator, in order to put in evidence the effectiveness and the actual implementation of the proposed optimization technique.     

Unified closed-form expression of logit and weibit and its extension to a transportation network equilibrium assignment

This study proposes a generalized multinomial logit model that allows heteroscedastic variance and flexible utility function shape. The novelty of our approach is that the model is theoretically derived by applying a generalized extreme-value distribution to the random component of utility, while retaining its closed-form expression. In addition, the weibit model, in which the random utility is assumed to follow the Weibull distribution, is a special case of the proposed model. This is achieved by utilizing the q-generalization method developed in Tsallis statistics. Then, our generalized logit model is incorporated into a transportation network equilibrium model. The network equilibrium model with a generalized logit route choice is formulated as an optimization problem for uncongested networks. The objective function includes Tsallis entropy, a type of generalized entropy. The generalization of the Gumbel and Weibull distributions, logit and weibit models, and network equilibrium model are formulated within a unified framework with q-generalization or Tsallis statistics.     

Multilevel analysis of daily time use and time allocation to activity types accounting for complex covariance structures using correlated random effects

In this paper multilevel analysis is used to study individual choices of time allocation to maintenance, subsistence, leisure, and travel time exploiting the nested data hierarchy of households, persons, and occasions of measurement. The multilevel models in this paper examine the joint and multivariate correlation structure of four dependent variables in a cross-sectional and longitudinal way. In this way, observed and unobserved heterogeneity are estimated using random effects at the household, person, and temporal levels. In addition, random coefficients associated with explanatory variables are also estimated and correlated with these random effects. Using the wide spectrum of options offered by multilevel models to account for individual and group heterogeneity, complex interdependencies among individuals within their households, within themselves over time, and within themselves but across different indicators of behavior, are analyzed. Findings in this analysis include large variance contribution by each level considered, clear evidence of non-linear dynamic behavior in time-allocation, different trajectories of change in time allocation for each of the four dependent variables used, and lack of symmetry in change over time characterized by different trajectories in the longitudinal evolution of each dependent variable. In addition, the multivariate correlation structure among the four dependent variables is different at each of the three levels of analysis.     

Dynamic network equilibrium for daily activity-trip chains of heterogeneous travelers: application to large-scale networks

Applications of dynamic network equilibrium models have, mostly, considered the unit of traffic demand either as one-way trip, or as multiple independent trips. However, individuals’ travel patterns typically follow a sequence of trips chained together. In this study we aim at developing a general simulation-based dynamic network equilibrium algorithm for assignment of activity-trip chain demand. The trip chain of each individual trip maker is defined by the departure time at origin, sequence of activity destination locations, including the location of their intermediate destinations and their final destination, and activity duration at each of the intermediate destinations. Spatial and temporal dependency of subsequent trips on each other necessitate time and memory consuming calculations and storage of node-to-node time-dependent least generalized cost path trees, which is not practical for very large metropolitan area networks. We first propose a reformulation of the trip-based demand gap function formulation for the variational inequality formulation of the Bi-criterion Dynamic User Equilibrium (BDUE) problem. Next, we propose a solution algorithm for solving the BDUE problem with daily chain of activity-trips. Implementation of the algorithm for very large networks circumvents the need to store memory-intensive node-to-node time-dependent shortest path trees by implementing a destination-based time-dependent least generalized cost path finding algorithm, while maintaining the spatial and temporal dependency of subsequent trips. Numerical results for a real-world large scale network suggest that recognizing the dependency of multiple trips of a chain, and maintaining the departure time consistency of subsequent trips provide sharper drops in gap values, hence, the convergence could be achieved faster (compared to when trips are considered independent of each other).     

A stochastic optimization model for transit network timetable design to mitigate the randomness of traveling time by adding slack time

Transit network timetabling aims at determining the departure time of each trip of all lines in order to facilitate passengers transferring either to or from a bus. In this paper, we consider a bus timetabling problem with stochastic travel times (BTP-STT). Slack time is added into timetable to mitigate the randomness in bus travel times. We then develop a stochastic integer programming model for the BTP-STT to minimize the total waiting time cost for three types of passengers (i.e., transferring passengers, boarding passengers and through passengers). The mathematical properties of the model are characterized. Due to its computational complexity, a genetic algorithm with local search (GALS) is designed to solve our proposed model (OPM). The numerical results based on a small bus network show that the timetable obtained from OPM reduces the total waiting time cost by an average of 9.5%, when it is tested in different scenarios. OPM is relatively effective if the ratio of the number of through passengers to the number of transferring passengers is not larger than a threshold (e.g., 10 in our case). In addition, we test different scale instances randomly generated in a practical setting to further verify the effectiveness of OPM and GALS. We also find that adding slack time into timetable greatly benefits transferring passengers by reducing the rate of transferring failure.     

Optimal design of electric vehicle public charging system in an urban network for Greenhouse Gas Emission and cost minimization

In this paper, we address the optimization problem of allocation of Electric Vehicle (EV) public fast charging stations over an urban grid network. The objective is to minimize Greenhouse Gas Emissions (GHG) under multiple constraints including a limited agency budget, accessibility of charging stations in every possible charging request and charging demands during peak hours. Additionally, we address bi-criteria problems to consider user costs as the second objective. A convex parsimonious model that depends on relatively few assumptions and input parameters is proposed and it is shown to be useful for obtaining conceptual insights for high-level planning. In a parametric study using a hypothetical urban network model generated based on realistic parameters, we show that GHG emissions decrease with agency budget, and that the reductions vary depending on multiple factors related to EV market and EV technologies. The optimal solutions found from the bi-criteria problems are shown to be close to the solution minimizing GHG emissions only, meaning that the emission minimizing policy can also minimize user costs.     

Investigating the impact of stochastic vehicle arrivals to optimal stop spacing and headway for a feeder bus route

Stop spacing and service frequency (i.e., the inverse of headway) are key elements in transit service planning. The trade‐offs between increasing accessibility and reducing travel time, which affect transit system performance, need to be carefully evaluated. The objective of this study is to optimize stop spacing and headway for a feeder bus route, considering the relationship between the variance of inter‐arrival time (VIAT), which yields the minimum total cost (including user and operator costs). A solution algorithm, called successive substitution, is adapted to efficiently search for the optimal solutions. In a numerical example, the developed model is applied to planning a feeder bus route in Newark, New Jersey. The results indicate that the optimal stop spacing should be longer that those suggested by previous studies where the impact of VIAT was ignored. Reducing VIAT via certain operational control strategies (i.e., holding/stop‐skipping, transit signal priority) may shorten stop spacing and improve accessibility. Copyright © 2014 John Wiley & Sons, Ltd.