Search

This paper develops a model to analyse hazmat shipments routing in the context of hazmat transportation between the specified origin-destination (OD) pair. A novel aspect of this model is the consideration of risk equity using the standard deviation, an established computation to assess equity. To solve the model, a two-phase method is developed, in which the multi-objective shortest path algorithm is used to obtain the Pareto-optimal paths set, and get the routes using estimation of distribution algorithm after paths choice. We then present a test problem of hazmat shipment with consideration of risk equity and discuss computational results.

In view of the increasingly prominent security issues of dangerous goods transportation, a distribution route optimisation model of dangerous goods is proposed. Firstly, the travel time reliability model and transportation risk model are established respectively. Besides, the multi-objective optimisation model of dangerous goods route is built based on the travel time reliability. Secondly, the genetic algorithm is designed to solve the optimisation model. Finally, a numerical case is used to verify the feasibility and effectiveness of the model and algorithm. Study result shows that the distribution route optimisation model of dangerous goods has an important theoretical value and practical significance of reducing the accident risk, increasing the time reliability and ensuring dangerous goods transportation safety.

As the transportation safety problem of dangerous goods is becoming more and more important, this paper establishes an optimisation model for the dangerous goods transport based on the improved ant colony algorithm. Firstly, the optimisation model of dangerous goods transport is designed. Secondly, the optimisation algorithm is designed based on the ant colony algorithm and genetic algorithm. Finally, the model and algorithm are verified by a case. The result indicates that the optimisation model and algorithm of dangerous goods transport have an important guiding significance on the safety of dangerous goods transport.

Aiming at hazardous materials transportation (HMT), vehicle routing optimisation models for single vehicle and multiple vehicle are proposed respectively, and the adaptive double populations genetic algorithm are constructed. Firstly, the goal functions of models are minimising the total risk, cost and the running time of hazardous materials vehicle. Then, the load constraint, max-risk constraint and time window constraint are considered. Finally, natural number is used for coding, double populations mechanism and adaptive weighted fitness allocation mechanism are adopted to calculate unit fitness, partial matched-crossover method is adopted for crossover operation, and the inversion mutation operator is adopted for mutation operation. Case study shows the model and algorithm are feasible, the vehicle routing strategy can provide direct reference for hazardous materials transportation decision-making departments and it is an effective way for prevention of hazardous materials transportation accidents.

This paper builds models of two passengers and multiple passengers sharing a taxi. Advantages and effects of taxi carpooling mode are analysed by simulation. The influences of payment ratio and carpool ratio on taxi carpooling effects are analysed. Relevant conclusions are obtained. Simulation results show that taxi carpooling mode can improve driver income, decrease passenger payment and increase passenger capacity obviously. Payment ratio and carpooling ratio have great influences on carpooling effects. Driver income and passenger capacity increase with increase of carpooling ratio. Change of payment ratio influences driver income and passenger payment simultaneously. The increase of carpooling ratio can help to develop carpooling advantages better. Appropriate payment ratio is the key to obtain good carpooling effects. When ρ is larger, appropriate θ can be found to ensure win-win for drivers and passengers, and improve taxi passenger capacity; when ρ is so small that no existing θ can ensure these effects, it needs to increase the number of taxis. These conclusions have a certain guiding significance to formulating taxi policy.

Hazardous materials transport network optimisation is the basis of ensuring the safety of hazardous materials transport. Considering the uncertainty of much basic information in hazardous materials transport system, this paper proposes the multi-objective chance-constrained programming model for hazardous materials transport network under uncertainty optimisation theory framework. Then, the paper builds the multi-objective hybrid intelligent algorithm to solve the model. The algorithm applies the stochastic simulation and fuzzy simulation to simulate uncertain parameters, adopts the priority coding way to code chromosome, applies the chromosome marker selection strategy to complete the selection operation, adopts the priority index crossover operator to ensure offspring's inheritance and advantages for the parent, uses the single parent vicinal swap method to complete mutation and applies the exclusive method to build dominating sets. Finally, the case study shows the model and algorithm are feasible.

In order to resolve the parking problem in the new district, a new site selection method for public parking lot is put forward. Firstly, the future parking demands are forecast by parking generation rate model. Secondly, the preliminary locations and quantity of the parking lot are planned by the analytical hierarchy process. Finally, the multi–objective site selection model is established and the genetic algorithm is designed. Based on the multi–objective model and the genetic algorithm, the final location and scale of parking lots are determined. Taking the new district of Lanzhou as an example, the locations and the berths of the parking lot are studied by the new method. The example shows the three–step method is feasible and it can help decision–makers plan the public parking lot in the new district.

An improved layered parallel particle swarm optimisation (ILPPSO) algorithm has been developed based on layered parallel tactics and catastrophe tactics. In the algorithm, the new inertial parameter was used, and two layers independent sub-swarms were cooperative optimised. The catastrophe occurred when inferior sub-swarms sink into a local optimum value, which can make the population evolve forward continuously, prevent an algorithm precocious refrain, insure obtain the global optimum solution. The experiment study shows that the ILPPSO algorithm can improve the searching ability, get the optimum result. Aiming at the expressway interchange jam problem, the traditional ramp fuzzy control algorithm is not ideal. This paper applies the ILPPSO algorithm to optimise the interchange ramp control parameters. The simulation results show that the ICPPSO algorithm was more effective than the standard PSO algorithm and genetic algorithm, and was able to carry out real-time parameters optimisation of interchange ramp control.

The paper was aimed to design a method to optimise the taxi ridesharing routes. Firstly, the paper built the objective function to minimise the total ridesharing fare of passengers, the total ridesharing travelling time of passengers and the total fuel charge of taxies under the constraints of driver's income, the travelling fare of passenger, the fuel charge of taxi and established the route optimisation model for taxi rideshare. Secondly, the paper designed non-dominated sorting genetic algorithm to solve the model. Finally, the part road network of Lanzhou Chengguan district was taken as an example for case analysis. The results show that the ridesharing routes can be obtained quickly by using the route optimisation model and algorithm and adopting the ridesharing routes can increase the income of taxi drivers as well as reduce the travelling fare of the passenger.

Hazardous materials transportation vehicles may become one of the main attack goals under the background of terrorist attacks. This article gives an optimisation method of emergency service facility location for a specific counterterrorism area. Firstly, the multi-objective optimisation model of emergency service facility location is proposed through maximising cover risks, minimising emergency response time, emergency facilities fixed costs and transportation costs. Secondly, the model is solved with an improved genetic algorithm, which use 0-1 encoding method to code chromosome, use roulette with the optimal preservation strategy to conduct choose operation and use adaptive weighted distribution method to assign weight. Finally, a case study with 12 nodes and 21 roads is given to indicate that the optimisation model and algorithm are feasible.

In order to relieve the traffic congestion, especially the traffic burden of the artery in the urban road network, this paper proposes an arterial traffic coordinated control optimisation method by using the cosine modified adaptive genetic algorithm. Firstly, this paper considers the importance of different directions traffic release of intersections coordinated phase, and takes the number of vehicles within the maximised green wave bandwidth as the objective to establish the traffic coordinated control model. Secondly, the cosine modified adaptive genetic algorithm is applied to solve the model. Finally, the traffic coordinated control system including six consecutive intersections is designed in Tianshui Road, Lanzhou, China. The optimisation result shows that the cosine modified adaptive genetic algorithm has a good performance in the optimisation of the traffic coordinated control system.

For logistics distribution route problem, this paper presents an improved genetic algorithm, combined with the thought of tree coded, to solve single vehicle logistics distribution route problem. The algorithm adopted Prufer array coding reducing three units length of chromosome, and the decoding method did not increase time complexity of chromosome decoded to calculate objective function value when compared with the traditional coding method which coded chromosome according to sequence traversing each node. In order to prevent the feasibility of solution being destructed by the traditional crossover and mutation operators, a single transposition operator and an ortho-position exchanging operator were also designed. The following cases show that the algorithm has a higher solving efficiency and is a great help to solve large-scale route optimisation problem.

Traveller route choice behaviour is influenced by many uncertain factors, which include both outer stochastic factors from road network and inner factors from traveller psychology. Traveller route choice behaviour is dynamic in practical travel process, namely traveller can change route on the way. Considered the characteristics of bounded rationality in human decision making process and change of route choice on the way, a dynamic route choice model is established based on prospect theory, and the method of dynamic route choice which is more suitable to human thinking habits and actual travel characteristics is proposed in this paper. The method allows the traveller to adjust the route according to road situation at any time. Decision making process reflects that the influence of traveller reference point on the decision result. Reference point changes with the situation of road sections passed. The examples are given to verify the validity and explain application of the method. The result shows that traveller can change route on the way when he meet congestion on the section passed, and if traveller's psychological expected time can not be met, he will choose risky route, by which the probability of congestion is higher. The method describes traveller route choice behaviour process better and reflects the true situation of road network. It is beneficial to predict traveller behaviours and changes of traffic network situation, and have certain guiding significance for traffic network planning and traffic intelligent control.

In order to insure transportation safety and reach destination rapidly, the best transportation route of hazardous materials is studied with multi-attribute decision-making theory. The train loaded with the hazardous material is one kind of dynamic hazardous source, once it encounters the accident, it can cause the huge threat to the people’s lives and properties. The scientific and reasonable choice for transportation route is the essential condition which safeguards the transportation security of hazardous materials. Under the hybrid language environment, the triangular fuzzy number and the interval number are used to express the uncertain variable, then the Lagrange function is established and the weighting vector is obtained by calculating partial derivative, and the best transportation route is ascertained through calculating the route projection operator value which displays on the ideal projection. The example confirms the decision-making method is feasible. The method is helpful for the relevant department to choose the reasonable transportation route under the hybrid language environment.

Urban traffic congestion is one of the most severe problems of everyday life in many cities. In an effort to deal with this problem, traffic control system technologies have concentrated in recent years on dealing with urban congestion. In the paper, a compound mechanism particle swarm optimisation (CMPSO) algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of city latent ringlike road traffic control system. In the algorithm, the tabu search algorithm and the particle swarm algorithm are combined effectively, and the tabu can offset the local optimums and avoid searching one solution repeatedly. Simulations show the CMPSO algorithm cannot only avoid premature convergence effectively, but also has powerful optimisation ability and higher precision. It makes the rate of delay and numbers of stopping reduce obviously.

In order to select the best vehicle scheduling route for hazardous materials, a new method based on uncertain operator is proposed. On the foundation of establishing the indices of hazardous materials’ vehicle scheduling routes, some basic theories of Uncertain Linguistic Hybrid Aggregation (ULHA) operator are used and the multi-attribute vehicle scheduling route decision-making method based on ULHA is set up. Finally, an example shows the method is feasible.

This study focuses on how to determine the optimum transportation route for hazardous materials under the certain, fuzzy or stochastic environment. On the basis of analysing the transportation route selection problem of hazardous materials (TRSP-HM), three objectives are presented and the multi-objective routing programming model (MRPM) for hazardous materials transportation (HMT) is put forward under the certain environment, and an improved label algorithm is proposed to solve the MRPM. After defining the maximum-chance optimum route and the α -optimum routes, the multi-objective routing chance-constrained programming model (MRCPM) and multi-objective routing dependent-chance programming model (MRDPM) for HMT under the fuzzy or stochastic environment are established respectively. Then, the integration intelligent algorithm is developed to solve the proposed models, which integrates the fuzzy simulation, neural networks, stochastic simulation and genetic algorithm. Finally, the proposed models and algorithms are successfully tested with the help of two real cases.

When selecting an optimal route for hazardous materials transportation, many factors are needed to be considered. Through minimising transportation risk and operation distance, multi-objective travelling salesman problem (MO-TSP) model for hazardous materials transportation route is established. The natural chromosome encoding is used to encode and the roulette and optimal saving strategy are combined for selection, the order crossover is used for crossover operation to improve the traditional genetic algorithm. Then the improved genetic algorithm is used to solve MO-TSP model of hazardous materials transportation route. Finally, the correctness and effectiveness of the model and algorithm are verified with a case. This approach can help decision-makers determine reasonable transportation route for the hazardous materials transportation.

The location problem is closely related to people’s lives and property, and the harmonious development of society. Based on the discussion of location principle, to meet the demand of emergency rescue point as large, the mathematical model for location problem of emergency service facilities is established. And then the Genetic-Simulated Annealing Algorithm (GSAA) for solving this model is designed, which enriches searching behaviour in optimisation process, and has strong abilities of exploration and exploitation in large real search spaces. In this algorithm, genetic algorithm controls the search direction, and simulated annealing algorithm plays an important role in local optimum convergence. Case study shows that the algorithm is efficient, and it not only gets optimal location results quickly and accurately, but also clearly gets the afflicted points that are covered by the selected facilities. So the model and the algorithm have strong practicability.

In order to overcome the drawback of basic particle swarm optimisation algorithm, such as being subject to fall into local optimisation and being poor in performance of precision, a chaos multi-population particle swarm optimisation algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of transportation continuous network design problem. In this algorithm, multi-population parallel tactics are introduced to improve the global optimising ability and the chaotic search which behaves well in local searching is introduced to improve the solution. The transportation continuous network design problem is solved based on chaos multi-population particle swarm optimisation algorithm. Two examples are presented to compare the proposed method with some existing algorithms. The simulation results show the new particle swarm optimisation algorithm can effectively alleviate the problem of premature convergence and strengthen the global searching ability. The algorithm presented in the paper can apply to solve the large scale traffic design problems.

On the basis of analysing the unusual objectives of hazardous material transportation (HMT), a new multi-objective optimisation model for hazardous material transportation (MOM-HMT) is established, which takes into account transportation risk, operation time, the number of sensitive population, risks fairness and multi-batch transportation simultaneously. Then a fuzzy adaptive weighted genetic algorithm (FAWGA) is set up to solve the MOM-HMT by designing priority-based encoding method, partial matching crossover, fuzzy logic control and adaptive weighted assignment mechanism. Finally, the model and algorithm are applied to a real case. The study results show the new model is feasible and the improved algorithm genetic is more effective than the standard genetic algorithm and the improved ant colony algorithm.