As green logistic emerged as a new trend, green vehicle routing problem (GVRP) has received wide attention from related fields, but literature reviews on the latest research of GVRP remain rare. Awared of this fact, this paper is intented to review several typical GVRP models and their solving algorithms. Firstly, an elementary GVRP model and several fuel consumption/carbon emission measuring methods are briefly described. Secondly, according to the optimization of environmental benefit and the composition of objective function, GVRP models are classified into tree types, i.e. fuel consumption/carbon emission minimization VRP, comprehensive cost minimization VRP and multi-objective VRP. Each model is discussed from four aspects, namely optimization objective, factors influencing fuel consumption/carbon emission, measurement models of fuel consumption/carbon emission and constraints. Then, some solving methods about GVRP such as exact algorithms, heuristic algorithms and metaheuristic algorithms are briefly introduced, and several widely used metaheuristic algorithms are analyzed. Finally, through presenting new applications of GVRP in just-in-time logistics distribution, cold chain logistics distribution, electric vehicle logistics distribution and joint logistics distribution, this paper points out the growing trend of theory and practical method of GVRP.

The frequent shipping activities have caused severe environmental problems. To realize the goal of emission reduction in shipping, it is important to implement a systematic analysis on how to achieve green ports and green shipping. This paper summarizes the literature on energy savings and emission reduction in shipping from three perspectives, i.e., green ports, green shipping, and green maritime policies. The green ports issues include port management and port technologies; the green shipping issues analyze shipping management and shipping technologies; the green maritime policy issues cover some existing policies and measures and their impacts on shipping industry and environment. This paper also identifies some research gaps and outlines some potential research opportunities, including green port oriented studies, green shipping oriented studies, and emission policy oriented studies.

From the perspective of reducing the energy consumption of train and utilizing the regenerative braking energy, the energy-efficient optimization problem of urban rail transit timetable is proposed. Considering the constraints such as the operation time between stations, total operation time and headway, the energy-efficient optimization model of timetable is established. And the method based on genetic algorithm is designed by the operation time allocation strategy between stations based on the operation curve set and the strategy that rationally manages the departure time of the trains to utilize the regenerative braking energy in the power supply sections. Taking the energy-efficient timetable of Guangzhou Metro Line 8 as an example, the effectiveness of the optimization method is verified. The results show that the regenerative braking energy utilization rate of the optimized timetable increases by 12.2%, the total energy consumption decreases by 10.9%, and has a good energy saving effect.

Vickrey first proposed an endogenous departure time decision model using the deterministic queuing theory in 1969, called bottleneck model. At equilibrium, all travelers have the same travel cost regardless of their departure time. The bottleneck model proposed by Vickrey fifty years ago used the trip as the unit of analysis without considering the correlation between trip and activity connected by a trip. However, the activity-based bottleneck model recently proposed has considered the impact of time-varying activity utility on the commuter's departure time decision. The queuing delay at a bottleneck is purely a deadweight loss to the society. Congestion pricing is an effective economic measure to reduce bottleneck queuing delay, and the bottleneck model is a powerful tool for investigating congestion pricing problems. This paper provides a review of the research progress of the bottleneck model, introduces the basic theories of standard bottleneck model and activity-based bottleneck model, and analyzes and compares two models' equilibrium solutions and their efficiencies in eliminating bottleneck queuing delays under the optimal dynamic time-varying toll scheme and optimal step toll scheme.

For tripartite agents of online ridesourcing platform, drivers and passengers, the queuing theory and the birth and death process theory are used to describe the driver state transition process (idle-busy) in the car-hailing platform. With the ridesourcing price and revenue sharing between ridesourcing platform and driver as decision variables, the supply and the demand functions of driver's willingness to join the ridesourcing operation and the passengers' willingness to ride are both proposed. On this basis, the ridesourcing platform pricing models to maximize the social welfare under the static pricing strategy and the dynamic pricing strategy, are both proposed from angle of government regulation, and the algorithm for solving the model is also designed; In order to ensure that the theoretical model results be able to guide or supervise the pricing of the ridesourcing platform, the existence of market equilibrium of ridesourcing platform are proved, and the social welfares of static pricing strategy and the dynamic pricing strategy are compared. The research results show that the social welfare under dynamic pricing strategy is greater than the one of static pricing strategy under finite market size, while the social welfares under the dynamic and the static pricing strategy is equal under the large-market limit. The proposed model can not only solve the static and the dynamic optimal prices of ridesourcing platform, but also investigate the impacts of average ride time, the behavior of drivers and passengers choosing ridesourcing platform on pricing of the ridesourcing platform, thus provide theoretical foundation for the pricing and supervisation of ridesourcing platform.

In view of competition between the online car-hailing service and taxi service, this paper uses the waiting time to characterize the service quality that affects the demand and considers two scenarios, i.e., the service quality of online taxi service is higher or lower than that of taxi service, in order to explore the condition of coexistence of the two services and the equilibrium pricing. The result shows that the two services can coexist with a higher differentiation when the total network externality is positive. While, when the total network externality is negative, the two services can coexist without other constraints. This paper also uses numerical examples to analyze the influence of the factors such as the waiting time of the online car-hailing service on the consumer surplus and the coexistence condition of the two services. In addition, this paper characterizes the taxi drivers' profit in the competitive market based on the reference dependence theory. And it proposes the regulation policy of the online car-hailing service according to the difference of service quality. The conclusion provides several managerial implications for pricing and regulation strategy of the online car-hailing service.

According to the destination, travel behavior during rush hour generally includes: Commuting to workplace and to school. With the rise of private car ownership, it is increasingly common for family members to travel from the place where they live to multiple destinations during the morning rush hour. Based on the Y-network, considering individual and household commuters, this paper use Vickrey's bottleneck model to analyze commuters' departure time choice. Under user equilibrium, no commuter can unilaterally change his behavior choice to reduce the cost. Finally, the theoretical conclusion is verified and analyzed by numerical examples.

With the rapid development of station-based one-way electric-car sharing, the temporal and spatial imbalanced distribution of cars resulted from the directional feature of requirements is becoming increasingly prominent. A transportation problem of station-based one-way shared electric-cars with recharging scheduling is studied. This problem considers the constraints of the remained mileage of cars and the working hours of employees with the objective criteria of total transportation costs. It simultaneously decides the source and destination stations of cars and the assigned employees so that the distribution of cars among stations is balanced and that the cars with low level of batteries are parked at the stations with charging piles. A 0-1 nonlinear programming model is built. In order to solve the model conveniently, aiming at the homogeny of employees, three valid inequalities about single transportation and the total routes are proposed to eliminate symmetry between solutions. The aforementioned mathematical formulations are validated based on randomly generated instances. The results indicate that all the three valid inequalities can help to solve the mathematical model and that the simultaneous application of the three valid inequalities can save about 50\% of solving time for medium- and large- scaled instances.

In travel behavior research, household travel behavior in the morning peak hours has always been a research hotspot. Recently, due to the high cost of ownership of cars and purchase restriction policy, it has become more and more common for household members to share a car to travel in the morning. A typical household travel is that commuters have to drive their children to school first and then go to work. In this paper, firstly, the decision-making of departure time for household travels in morning peak hours is analyzed based on activity utility. Secondly, the disutility of household travel and the utility of household members' activities at home, school and workplace are applied to construct a net utility function of household travel. Thirdly, a household commuting equilibrium model during the morning peak period was built by this net utility function of household travel. Finally, based on the equilibrium model, an optimal dynamic toll strategy can be obtained, and then a one-step optimal congestion toll model is proposed. Using the proposed model, the impact of the expected time interval between work and school on the total system utility, toll level and toll window is discussed. This model can help traffic manager maximize the total utility of the system by setting the optimal time interval.

As electric vehicles are becoming increasingly popular, more and more logistics companies deliver products by both conventional fuel vehicles and electric vehicles. Based on the difference between maximum capacity, maximum travel distance and operating cost of conventional vehicles and electric vehicles, and the charging behavior of electric vehicles, electric vehicle routing problem with time window and mixed fleet is examined. An integer programming model is presented and it is decomposed into the main problem and the sub-problem based on the Dantzig-Wolfe decomposition principle. A heuristic rule and genetic algorithm are proposed to generate the initial solution quickly for both small-scale instances and large-scale instances respectively. The branch and price algorithm is developed to obtain the optimal solution. The performances of proposed model and algorithm are validated by comparison with CPLEX. Finally, sensitivity analyses of the influence of number of electric vehicle, vehicle capacity, battery capacity, charging rate on the total cost are performed, and managerial insights are obtained.

The rapid development of the cruise industry has brought a series of marine environmental problems. In the process of cruise navigation, cruise operation and passengers on board will produce an uncertain amount of waste. Generally, the total amount of waste is far greater than its capacity. It is necessary to dispose of waste in multiple ports of the line, which requires a certain fee to be paid to the port. Therefore, this paper studied a waste disposal problem for cruise ships to decide which port to sign and how much waste should be discharged under the uncertain amount of produced waste. These decisions affect the operating costs of cruise companies, so it is necessary to calculate and analyze scientifically through some optimization models. Based on the theory of system engineering, this paper analyzed the background of the above-mentioned decision-making problems and proposed three models successively by using the theory and tools of mathematical programming. The models include a deterministic model, a stochastic programming model that applied to arbitrary probability distributions of emission parameters, and a three-stage robust optimization model that can cope with the uncertain parameter interval. Then, considering the above problems and the characteristics of the model, this paper designed a Tabu search (TS) and particle swarm optimization (PSO) algorithms to solve large-scale problems in stochastic and robust models, respectively. Extensive experiments in this paper validated the effectiveness and efficiency of the proposed models and algorithms. The decision-making method of cruise waste emissions proposed in this paper has certain application value and guiding significance for the development of the current global green shipping industry.

This paper presents an activity-based commuting model to study the dynamic user equilibrium during morning rush hour commuting. In this model, commuters can use three travel roles:Self-drive, carpool driver and carpool passenger, i.e., two travel modes:Self-drive and carpool. In the chain of activities, it is assumed that the commuter generates utility both at home and at work. Moreover, the extra carpool cost, other monetary cost and compensation cost are introduced into the commuting model. During the peak period, commuters maximize their travel utility by choosing the departure time and travel mode. Under user equilibrium, each traveler cannot make their travel utility greater by unilaterally changing their behavior choices. Furthermore, from the perspective of eliminating the queuing delay, this paper deduces the optimal time-varying toll. Finally, a numerical example is conducted to analyze the theory conclusions.

Shared bike can be retrieved and parked at any station in the bike-sharing system. One of the most important challenge is the demand for bikes is always deviated from the supply. This paper proposed a new user-based method for shared bike repositioning problem. In the proposed problem, some excess bikes are set as lucky bikes which are relocated by users to unsaturated nodes. Users can gain the monetary rewards after the completion of the relocations. A mixed integer programming model is formulated to minimize the total transportation cost, the total reward payouts and the total penalties due to unmet demand. To solve this problem, a hybrid tabu search is developed. The numerical experiments show that the user-participating mechanism can effectively reduce the total cost. The hybrid tabu search can effectively solve the large networks of the proposed problem.

Dynamic ride-sharing system is a newly-rising intelligent travel system, which could effectively integrate transportation resources, alleviate congestion and reduce pollution via real-time matching among riders and drivers. User experience plays an important role in the sustainable development of dynamic ride-sharing system. However, the traditional dynamic ride-sharing system only cares operational efficiency, ignoring user experience. In view of this, this paper develops a new dynamic ride-sharing system considering user experience, by introducing three mechanisms, that is, real-time feedback, priority allocation and fixed-match elimination. The decision-making process of the system is divided into three stages. Firstly, to find the real-time optimal matching solution, a maximum weight bidirectional matching model is presented based on priority allocation mechanism. Secondly, users are informed of the matching decision based on real-time feedback mechanism. Thirdly, the matching solution is evaluated based on fixed-match elimination and the latest departure time mechanism. Numerical results show that, compared with the traditional system, the proposed system could not only reduce the users' waiting time sharply, improve the user experience, but also enhance the system efficiency.

How to remove the bottleneck at a bus transit hub quickly is a difficult problem for commute residents. This paper combines the bottleneck model and the activity-based approach to study commute behavior in the evening rush hours at bus hubs, so as to solve the problem of time allocation between commuters' activities and trips. Based on the bottleneck model and considering on-board congestion, traffic behavior is related to traffic activity. Dynamic equilibrium is gained by introducing bus congestion cost and choosing departure time according to different utility functions. Properties under the equilibrium conditions are obtained to explain the traffic phenomenon of commuters queuing at the bottleneck in the evening rush hours. Compared with traditional bottleneck models, the activity-based bottleneck model is more abundant and has more complicated departure time selections. Results show that the more sensitive the commuters are to on-board congestion, the more they will try to avoid the peak travel. For greater net utility, commuters may choose to stay in the workplace for longer periods of time and peak hours may be delayed.

In the paper, we propose to set up shared parking spaces near the private parking lot of office buildings to alleviate the contradiction of insufficient parking space in the city center. Firstly, a bottleneck economic model is established, which includes both private parking lot and shared parking lot with acceptable walking distance. An iterative algorithm is designed to solve the analytic solutions of average travel cost, average queuing time, total travel cost and total parking profit. The research shows that setting up shared parking spaces near private parking lot can effectively reduce total travel cost and alleviate traffic congestion, but too much construction will reduce the travel efficiency of the system, resulting in new congestion; from the perspective of the optimal total travel cost, managers should adopt policies to encourage shared parking (such as appropriately reducing the price of shared parking space or increasing subsidies); from the perspective of total parking profit, shared parking spaces should be priced reasonably according to the charging standard of private parking spaces. Therefore, the mode of setting up shared parking spaces can be used as an effective means of traffic management. By optimizing the number and price of shared parking spaces, the effective management objectives can be achieved.

This paper studies the morning commuting behavior in a bottleneck-constrained corridor when the autonomous and regular vehicles coexist, considering the behavioral differences between them in both driving and parking simultaneously. The following three cases are considered: 1) autonomous vehicles only improve the road capacity; 2) autonomous vehicles only reduce the value of time VOT; 3) both affect the road capacity and the VOT. All possible equilibrium travel patterns in each case are analyzed, and it is proved that commuters with the later travel mode have a lower equilibrium travel cost. Finally, numerical examples are presented to illustrate the effects of the autonomous vehicle penetration on travel costs, delay costs and empty run costs, and the impacts of changes in road capacity and VOT on the total system cost.

This paper analyzed travelers' route choice inertia in stochastic network and proposed an inertia-based route choice model. The inertia-based route choice model included different inertial behavior mechanisms and could be reduced to several route choice models in the literatures by setting special values to the parameters. The proposed route choice model was further applied to establish the stochastic-network inertial user equilibrium model as well as its equivalent non-linear complimentarity problem. The paper also conducted numerical examples to illustrate the inertial route choice model as well as the traffic assignment model.

By analyzing the impact of traffic information on travelers' route choice behaviors, this paper developed a mixed user equilibrium model. And in the mixed user equilibrium model, we considered two types of travelers:Inertial choice travelers and non-inertial choice travelers. Then, numerical examples were provided to illustrate the application of the proposed mixed user equilibrium model, and explored whether traffic information would help to relieve congestion when the travelers' route choices are inertial. The results showed that the traffic guidance information can influence travelers' route choice inertial behavior, but can also lead to increase the system's total travel time. Moreover, an appropriate permeability of traffic information can help to reduce the system's total travel time and make the system tend to optimize. Such an understanding has important implications on traffic information compliance.

A competitive transportation system with a mass transit (subway) parallel to a road way constrained by a bottleneck is considered. All travelers are divided into two groups. All travelers of one group have cars, and the other group have no cars. Travelers with cars can choose driving car or taking the subway, and travelers without cars can only take the subway. Under the optimal charging of bottleneck road, this paper analyzes five strategies to return the congestion charge revenue for public transportation travelers considering the body congestion cost of carriage in subway, and introduces the distribution proportion of the congestion charge revenue which takes into account the equity of individual travel cost and the efficiency of road traffic system, and verifies the theoretical results through numerical examples.

Traffic management strategies are capable of adjusting the traffic flow distribution among several travel modes so as to achieve the system optimum. This study investigates the influence of combined management strategy of tradable credit scheme (TCS) and encouraging carpooling on the commuters' departure time and travel mode choices, and collaboratively optimizes the parameters for the management strategy to achieve the system optimum, such as the coefficient for encouraging carpooling, the amount of credits charged on each vehicle and the capacity ratio for HOV lane. The results show that, after introducing the combined management strategy, the equilibrium credit price can be determined uniquely, in the uniform case, the lower is the coefficient for encouraging carpooling; the wider is the feasible region for the amount of credits charged on each vehicle, and the system optimum solution occurs when the capacity ratio for HOV lane equals a special value, and the amount of credits charged on each solo vehicle varies with the coefficient for encouraging carpooling along a specific relationship, and in the time-varying case, the improvement of total travel cost is higher but hard to operate it. This study can provide references for administrator implementing effective traffic management strategies.