Transportation vehicle scheduling optimization method based on improved multi-layer coding genetic algorithm

With the acceleration of urban development in China, the development of urban traffic system should be improved. Therefore, there are many problems such as traffic congestion, resource shortage and environmental pollution. Transportation is one of most basic needs for the survival and development of human society. Urban traffic planning is of great significance to the sustainable development of modern cities. It should be noted that urban traffic planning is only a part of urban planning, but there is a very close relationship between them. If in the process of urban traffic planning, the value of urban traffic planning can not be brought into full play if the coordination relationship between them is not clarified. In order to avoid the above problems, traffic management departments have established various traffic management information systems with the help of wireless network optimisation technology. The data structure and supported data types of each system are also different, which results in the operation of "island" management information system, therefore, this paper constructs a framework of urban traffic planning based on wireless network optimisation model and cognitive sustainable data retrieval. We optimise the communication model to construct the efficient system.

We are more and more close to the time when a higher number of autonomous vehicles are appearing in road traffic. The number of unanswered questions does not diminish but grow. One such issue is the role of autonomous vehicles in public transport. When talking about autonomous vehicles we often think of only cars and we think less about self-driving buses. But the economic potential inherent in autonomous buses is huge. In the Hungarian vehicle and crew scheduling practice (and also in other countries) the one driver-one vehicle control is typical. This method closely links the vehicles and the drivers. Vehicles should therefore adapt to the rest time of the crew and the employment rules. Unused reserves are generated in the system. Autonomous vehicles can release this overcapacity. Thanks to that, fewer vehicles can carry out public transport tasks and we can save extra rides. It also provides a solution to the lack of drivers, which is a basic problem in many countries. In our study we show the reserves that can be recovered from the system in the case of three Hungarian cities (Eger, Dunaujvaros, Győr). We show how much savings can be achieved by running autonomous buses in European cities with a population of 45 000, 54 000 and 130 000 inhabitants. The results are promising. In smaller cities we could achieve about 20% of economical savings but in bigger cities 40% is also realistic. Our statements are based on only rough calculations and they try to help in preparation for the future.

Constructing the cyclist : Ideology and representations in urban traffic planning in Stockholm, 1930-70

In the last decade, urban freight demand has grown substantially, driven by the expansion of urban population and industrial activities. This has facilitated the requirements for urban freight traffic planning, especially forecasting freight traffic demand at the metropolitan scale. Corporate freight facilities, as a major form of land use that generates truck demand, are key to estimating the overall flow of urban freight traffic. Understanding how the locations of urban freight facilities change as cities grow can help build a solid foundation for formulating sustainable policies to manage urban freight traffic. Though previous studies have examined the concept and location of corporate freight facilities, they failed to systematically identify such facilities due to the lack of high-quality data sets. Our study aims to fill this gap by utilizing advanced data mining methods such as remote sensing and computer vision to recognize the location of freight facilities within Guangzhou city from 2014 to 2023. We then explore the spatiotemporal evolution patterns of such facilities and analyze the potential generation of freight vehicle trips out of them. By discovering hot spot areas of freight vehicle generation, analyzing latent traffic bottlenecks, and integrating the evolution patterns of freight facilities, this study could help predict future changes and provide recommendations for local urban freight traffic management from a long-term perspective. This paper offers a creative research pipeline, broadens the research horizons of integrated urban freight traffic planning, and has a positive significance for the study of sustainable freight traffic planning.

An optimal grid scheduling method considering coupling degree of nodes and duality of reciprocal inverse mapping

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

The database on particle number emission factors has been very limited to date despite the increasing interest in the effects of human exposure to particles in the submicrometer range. There are also major questions on the comparability of emission factors derived through dynamometer versus on-road studies. Thus, the aims of this study were (1) to quantify vehicle number emission factors in the submicrometer (and also supermicrometer) range for stop-start and free-flowing traffic at about 100 km h(-1) driving conditions through extensive road measurements and (2) to compare the emission factors from the road measurements with those obtained previously from dynamometer studies conducted in Brisbane. For submicrometer particles the average emission factors for Tora Street were estimated at (1.89 +/- 3.40) x 10(13) particles km(-1) (mean +/- standard error; n = 386) for petrol and (7.17 +/- 2.80) x 10(14) particles km(-1) (diesel; n = 196) and for supermicrometer particles at 2.59 x 10(9) particles km(-1) and 1.53 x 10(12) particles km(-1), respectively. The average number emission factors for submicrometer particles estimated for Ipswich Road (stop-start traffic mode) were (2.18 +/- 0.57) x 10(13) particles km(-1) (petrol) and (2.04 +/- 0.24) x 10(14) particles km(-1) (diesel). One implication of the conclusion that emission factors of heavy duty diesel vehicles are over 1 order of magnitude higher than emission factors of petrol-fueled passenger cars is that future control and management strategies should in particular target heavy duty vehicles, as even a moderate decrease in emissions of these vehicles would have a significant impact on lowering atmospheric concentrations of particles. The finding that particle number emissions per vehicle-km are significantly larger for higher speed vehicle operation has an important implication on urban traffic planning and optimization of vehicle speed to lower their impact on airborne pollution. Additionally, statistical analysis showed that neither the measuring method (dynamometer or on-road), nor data origin (Brisbane or elsewhere in the world), is associated with a statistically significant difference between the average values of emission factors for diesel, petrol, and vehicle fleet mix. However, statistical analyses of the effect of fuel showed that the mean values of emission factors for petrol and diesel are different at a 5% significance level.

Fully autonomous intersections, where traffic lights are not needed, are a long-term goal in urban traffic planning. In these types of intersections, the priority rules and the order in which vehicles can cross the intersection are determined by wireless communication. Eliminating the human factor can lead to fewer incidents; however, to achieve this, the system must operate perfectly. In the case of autonomous intersections, communication between vehicles and infrastructure must be flawless, as must the execution of the desired maneuvers (for example, avoiding emergency situations). The aim of this research is to analyze autonomous vehicles, intersections, control methods, and how failures in different components of the specified infrastructure can lead to safety and security issues. Firstly, it is necessary to categorize the different types and elements of autonomous intersections and control methods. This includes analyzing the concepts and components of centralized and decentralized intersections, the components of self-driving vehicles, communication protocols, and the topics of safety, security, and cybersecurity in autonomous systems. Potential failures are identified using the Failure Mode and Effect Analysis (FMEA) method, which categorizes and evaluates the impact of different failures in the intersection system. This analysis provides an overview of potential failures and pinpoints components that pose the highest risk. In addition, this paper conducts a simulation-based analysis to assess the severity of some critical failures. Providing comprehensive solutions to such failures is beyond the scope of this work.

The growth in economic activities has led to an increase in car ownership, contributing significantly to traffic congestion in modern urban areas. The study investigated the impact of multi-story parking facilities on traffic congestion and environmental performance at three critical intersections in Baghdad: Al-Khulafaa, Al-Ameen, and Al-Maidan. Under existing conditions, all intersections suffered from high congestion levels, long delays, low travel speeds, and high emissions. Field data were collected using Metro Count devices and analyzed through SIDRA Intersection v9 software under two scenarios: the existing conditions and an alternative scenario incorporating structured parking and improved traffic control measures. Comparisons were made using key performance measures, including average vehicle delay, queue length, travel speed, fuel consumption, and pollutant emissions. In the alternative scenario, the findings showed significant improvements: travel speeds virtually doubled, queue lengths decreased by over 70%, and vehicle delays decreased by up to 87%. Additionally, all sites had a greater than 70% decrease in CO₂, CO, HC, and NOₓ emissions as well as annual fuel usage. The results show that drivers seeking open spots and on-street parking are the main causes of traffic congestion. The study concludes that incorporating multi-story parking facilities into urban traffic planning is a very successful way to increase mobility, reduce environmental impact, and boost the overall effectiveness of the road network in crowded central regions.

An efficient spatial-temporal transformer with temporal aggregation and spatial memory for traffic forecasting

Tensor-based models emerged only recently in modeling and analysis of the spatiotemporal road traffic data. They outperform other data models regarding the property of simultaneously capturing both spatial and temporal components of the observed traffic dataset. In this paper, the nonnegative tensor decomposition method is used to extract traffic patterns in the form of Speed Transition Matrix (STM). The STM is presented as the approach for modeling the large sparse Floating Car Data (FCD). The anomaly of the traffic pattern is estimated using Kullback–Leibler divergence between the observed traffic pattern and the average traffic pattern. Experiments were conducted on the large sparse FCD dataset for the most relevant road segments in the City of Zagreb, which is the capital and largest city in Croatia. Results show that the method was able to detect the most anomalous traffic road segments, and with analysis of the extracted spatial and temporal components, conclusions could be drawn about the causes of the anomalies. Results are validated by using the domain knowledge from the Highway Capacity Manual and achieved a precision score value of more than 90%. Therefore, such valuable traffic information can be used in routing applications and urban traffic planning.

This paper mainly analyzed on the importance of traffic survey to urban traffic planning. According to the content, it is divided into three categories: land-use survey, population and society economic survey, traffic special survey. We teased out the most used methods in traffic planning investigation, then compared the traditional traffic investigation methods with the modern investigation method that consists of big data, enumerated the modern information technology used in comprehensive traffic survey in recent years such as Beijing, Shanghai and Tianjin which affirmed the advantages of modern investigation methods and the fundamental role of traditional investigation methods. Finally, this paper put forward viewpoint of that human is the key role in survey from the aspect of thoughts, data, methods and analysis.

The scheduling problems for airport baggage transport vehicles are much influential to the quality of airport service. An optimal scheduling is very helpful to provide an efficient and safe airport operation and improve customers’ experience. To address the issue, in this paper, a novel genetic algorithm (GA) is proposed for the vehicles scheduling. To enhance the exploitation ability of GA, the algorithm is improved by considering both population diversity and population fitness simultaneously. In the proposed GA, a cooperative mechanism is employed to design the selection operation for genetic algorithm where both exploitation ability and exploration ability can be considered. Numerical experiments are conducted on widely used benchmarks, and several peer meta-heuristic algorithms are also used in performance comparison. To address the airport baggage transport vehicle scheduling problem, real data is adopted in the proposed algorithm for simulation. According to simulation results, the proposed algorithm is feasible and effective to obtain competitive performance and the airport baggage transport vehicles scheduling problem in is well addressed.

With the intensification of the contradiction between economic development, fossil fuel shortages, and severe environmental pollution, the development and widespread adoption of Electric Vehicles (EVs) have become an inevitable trend. The large-scale and disorderly charging of EVs connected to the grid will impose significant impacts on the power system, potentially leading to local overloads and threatening the security and economic operation of the grid. Therefore, this study investigates the coordinated optimization planning problem involving generators, EVs, and renewable energy sources (wind and solar). A spatiotemporal optimization strategy for EV charging scheduling is proposed. On the temporal scale, an optimal scheduling model based on unit commitment is established, aiming to minimize the operational costs of generators on the transmission grid side, PM2.5 emissions, total user charging costs, and the curtailment of wind and solar power. On the spatial scale, an optimal power flow-based scheduling model is developed to reduce distribution network losses, taking into account network security constraints and the spatial migration characteristics of EVs. The proposed EV charging scheduling strategy is simulated and analyzed on a power system model comprising a standard 10-machine transmission network and an IEEE 33-node distribution network. The results validate the effectiveness and superiority of the proposed spatiotemporal optimization scheduling strategy.