Bus Allocation Research Articles

A Model Based Method for Capacity Allocation in the Bus Transportation Industry

With the implementation of the dual carbon goal, electric vehicles have been rapidly promoted. However, in extreme conditions such as natural disasters, especially when power supply is insufficient, the widespread use of electric buses and the delisting of fuel powered buses may lead to insufficient transportation capacity in the region. This article establishes a calculation model for the allocation of buses of various power types to address this issue. Based on population size, the paper comprehensively considers the charging time and range of pure electric buses during use, and calculates the transportation capacity of pure electric buses and fuel buses respectively. Then, based on the rule of prioritizing the use of electric vehicles for transportation, when the capacity of electric buses is insufficient, fuel powered buses are used for replenishment. Calculate the minimum number of fuel buses required to ensure the basic transportation capacity for personnel transfer.

Applying modified-data mining techniques to assess public transportation vulnerable urban and suburban city areas

To guarantee the right to move for residents in areas where public transportation is insufficient, research is needed to identify vulnerable areas and prepare measures. This paper defines the vulnerable regions of public transportation within various city types in Korea. In order to identify appropriate areas to apply the Demand Responsive Transit (DRT), the regions with vulnerability were compared with a specific city (Yangsan-si) which already the DRT system was successfully adopted. To collect monthly bus data, web-data crawling method was performed and processed with coordinating program by matching GPS coordinate. The public transportation demand was predicted for each grid cell size (100 m, 250 m, and 500 m) by different methodologies. Various data mining models based on regression were analyzed to predict bus demand of vulnerable areas. Among models, a modified model was suggested to combine Automated machine learning models for high prediction performance. The modified model outperformed other methods as 0.685 and prediction performance was appropriate at 100 m rectangle grid. Regional characters of DRT bus allocation areas were extracted by K-means clustering method and differentiate urban and suburban types. The findings of this study provide valuable insights into conditions that DRT bus stop can be installed. The urban bus stop areas located in metropolitan cities and the suburban bus stop allocation areas located in countryside. The study results can be used as policy data for the successful introduction to prevent social exclusion and improve resident welfare in the future.

Optimal Allocation of Buses to Routes Using Linear Programming as a Tool in a Transport Service Authority: A Case Study of Kano State Transport Authority, Nigeria

In this paper, new profit maximization for Kano State transport authority resulting from optimal allocation of buses to inter-state routes is considered taking into consideration all the constraints associated. The problem was modeled using linear programming and the TORA (a software for solving linear programming problems) was used to obtained the solution to the modeled problem. The maximum objective value of ₦2,203,900.00 was obtained daily after 16 iterations and this a better result when compared to the current traditional or intuitive schedule by the authority that yielded ₦2,036,000.00 daily. This recommended schedule will yield additional ₦167,900.00 daily and over ₦5,000,000.00 monthly when implemented.

Integer Linear Program Model for Efficient Bus Scheduling Problem in Transport Management for Akwa Ibom Transport Company

Integer program was formulated in this work for efficient bus scheduling problem in Transport Company to obtain an optimal bus allocation system for its travelling routes in Nigeria. Integer programs were formulated for both the long and short routes categories. Transportation data from Akwa Ibom Transport Company (AKTC) was used to obtain an optimal bus schedule for the company with increase turnover in daily ticket collection by N2,327,500.00, N825,300.00 and N3,152,800.00 representing 34.78%, 10.60% and 21.78% respectively for the long routes, short routes and the overall routing system, while also meeting daily customers’ demand. Hence, the use of integer programming model is recommended for efficient vehicle scheduling and increase turnover in transport companies and personnel management system.

Deadheading minimization of the massive transportation system in the city of Cali Colombia

A model is presented for optimizing the bus allocation to depots or workshop yards (WY), minimizing the total daily fuel consumption of the system during empty runs (non-commercial), and considering the different types of buses, the WY capacities, the demand, and respecting the participation of each WY in the operation. The model is tested in a case study located in Cali and its results reveal significant cost savings opportunities by means of different strategies.

Linear programming application in optimal allocation of buses to inter and intra state routes from Katsina State Transport Authority Service, Nigeria

In this paper, we study Linear Programming application in optimal allocation of Buses to inter and intra state routes from Katsina State transport authority services to maximize profit and boost the state’s internally generated revenue. The problem was formulated from the data collected at their headquarters and by taking into consideration all the constraints. The TORA (a software for finding solution to linear programming problems) was used to obtained the solution to the model which yielded the maximum objective value of ₦729, 578 daily after 16 iterations. This gives better result when compared to the current initiative schedule by the authority that yields ₦584, 652 per day. By the recommendation schedule, the State will be getting additional ₦144,926.00 daily which translates to 24.8% daily profit increase and over ₦4,000,000.00 monthly when implemented.

The depot and charging facility location problem for electrifying urban bus services

• The depot and facility location problem is formulated for deploying electric and diesel buses. • Cost components in the transition from diesel buses to electric ones are evaluated. • Depot locations highlight the trade-off between deadhead mileage and land cost. • The required ratio of electric buses is a key factor in the total operational cost. • The bus fleet size affects the cost and robustness of system operation. The transition from diesel-consuming buses to electric ones entails the problem of determining the locations of bus depots, charging and maintenance stations, which can directly affect the operating cost and efficiency of a bus system. The problem also involves practical concerns, including the selection of fleet size (of electric buses), land acquisition, bus allocation, and the associated deadhead mileage. This study develops an optimization model to systematically locate depots and relevant facilities for deploying mixed types of vehicles. Further, a decomposition-based heuristic algorithm is proposed to enhance computational efficiency. A case study is performed using the data of a bus operator in Taiwan. Sensitivity and scenario analyses are conducted, evaluating major cost components and identifying the factors that affect the trade-offs between them. The proposed methodology enables bus operators/planners to closely and holistically examine their decisions during the transition toward electric bus systems.

Mixed electric bus fleet scheduling problem with partial mixed-route and partial recharging

Given the goal of reducing emissions and saving energy, an increasing number of transit agencies have proposed electrification plans for public transport buses. The two fundamental challenges to adopting electric vehicles in transit operations are the purchase of appropriate electric vehicles to establish the bus fleet and the creation of an efficient schedule and recharging plan. This paper examines the multi-depot and multi-vehicle type electric vehicle scheduling problem with partial mixed-route strategy and partial recharging policy. The partial mixed-route strategy proposed in this paper allows multiple transit routes to operate in a more cost-efficient way. Moreover, it takes into account the bus allocation problem of the transit network fleet to meet the parking restrictions of each depot. The problem is formulated in a mixed-integer programming model, and an adaptive large neighborhood search (ALNS) algorithm with new mechanisms specific to the problem is proposed to apply the model in a more efficient manner. The dataset of a real transit network in Nanjing is used for case study, and the performance of ALNS is tested by using randomly generated instances from the dataset. The results show that the proposed method is effective in finding high quality solutions and adopting partial recharging policy can reduce the fleet size and the total cost while providing advantages depending on the operational parameters of the schedule. In addition, comparison of two schedules using different partial mixed-route strategies shows that there may be two sides of adopting mixed-route scheduling.

Methods of Rational Allocation of Buses on Routes of Local Traffic

Methods of Rational Allocation of Buses on Routes of Local Traffic

Location of depots and allocation of buses to depots in urban road transport organisations: a mathematical model and greedy heuristic algorithm

Optimising the cost of operations is one of the major issues in any urban road transport organisations (URTOs). In this study, a decision problem on location of depots (adding new locations and removing existing ones) and allocation of buses to depots is considered, as observed in one of the major URTOs in India. The main focus of this research is to provide analytic methods to minimise the cost of operations comprising: 1) dead-kilometre cost; 2) fixed cost associated with introducing new depots; 3) salvage value due to closing the depots. To do so, a (0-1) mixed integer linear programming (MILP) model is proposed and its workability is demonstrated. In addition, a simple greedy heuristic algorithm is also proposed. A computational experiment is developed to understand the performance efficiency of the proposed greedy heuristic algorithm in comparison with the optimal solution. From the average and worst case analyses of the performance evaluation, it is observed that the proposed greedy heuristic algorithm provides near-optimal solution. The (0-1) MILP model and the efficient greedy heuristic algorithm proposed in this study can be used to help make better decisions on location of depots and allocation of buses to depots of URTOs in general.

Location of depots and allocation of buses to depots in urban road transport organisations: a mathematical model and greedy heuristic algorithm

Optimising the cost of operations is one of the major issues in any urban road transport organisations (URTOs). In this study, a decision problem on location of depots (adding new locations and rem...

A Strategy to Alleviate Rush Hour Traffics in Urban Areas Based on School-Trip Commute Information

As one of the main social issues in the 21st century, urban traffic congestion has become a severe challenge, especially during rush hours. Investigation indicates relationships between school-trip commutes, i.e., students' trips to and from school, and traffic congestion. Therefore, optimizing traffic in terms of the school-trip commute is considered as an important practice of smart cities. This article proposes a long-coding adapted genetic algorithm for large-scale school district replanning. Moreover, the fitness function takes into account both the congestion coefficient model and the grayscale difference model, which can reduce the traffic congestion as well as balance the traffic load on school-trip routes. Based on the above results, the school-trip route optimization is formulated as the school bus routing problem (SBRP) and the split demand school bus routing problem (SDSBRP). A max-min ant system with a pheromone smoothing mechanism is proposed for the SBRP and the SDSBRP. Considering the large scale of the problem, a high-compactness K-means algorithm is proposed to determine bus stops before school bus routing. Besides, a public bus allocation strategy is proposed to allocate a fleet of public buses to serve school-trip routes with low allocation costs. The proposed algorithms are tested on a realistic and challenging dataset from real world. Experimental research results show the advantage of our proposed strategies in reducing overall rush-hour traffic and optimizing school-trip routes.

Throughput Unfairness in Fair Arbitration Interconnection-Buses for Aerospace Embedded Systems

Currently, embedded systems are composed of processors, memories, and Intellectual Property Cores (IP Cores) interconnected to develop a set of specific tasks. Therefore, the selection of an appropriate interconnection architecture is critical in terms of system performance and functionality. A Network-on-Chip provides an efficient and scalable interconnection solution when there are a large number of elements in the system. However, the bus-based interconnection system remains the best option to connect a few cores. The bus arbiter uses an allocation policy to select which IP Core obtains access to the bus. The so-called fair policies ensure that all processors in the system have the same opportunity to access the bus. However, they fail to offer a fair share of the bandwidth or transmission rate, especially when there are heterogeneous IP Cores. As a study case, we analyze an embedded aerospace system for earth observation. Different IP Cores preprocess satellite images at distinct execution times -and unbalanced processing ratesaffecting the delivery rate of images to earth. We study the phenomenon of uneven bus transmission rates due to improper bus allocation using policies such as Round Robin, FIFO, and Lottery. Also, we propose a metric to compute the maximum number of IP Cores without bus saturation.

Modeling a Risk-Based Dynamic Bus Schedule Problem under No-Notice Evacuation Incorporated with Dynamics of Disaster, Supply, and Demand Conditions

Apart from private traffic, the evacuation of transit-dependent population is also an essential component of emergency preparedness, especially under no-notice evacuation scenarios with limit evacuation horizon. In literature, most bus-based evacuation models for no-notice evacuation are established under implicit assumptions of uniform evacuation horizon among different pick-up locations or fixed bus fleet in the evacuation area. These constraints will distance their models from real-world situations, where evacuation horizon is various due to spatial distribution of pick-up locations and fleet size of bus available for allocation will increase over time in no-notice evacuation. This research presents a risk-based bus schedule model which is differentiated from the vehicle routing problem (VRP) and bus evacuation problem (BEP) in literature, including the objective and the time-dependent parameters. A quantified definition of evacuation risk for pick-up location with concerns of disaster dynamics and time-varying supply-demand conditions is proposed in this paper as a criterion for bus allocation, also acting as a reflection of social equity to some extent. A notion of time-evolving disadvantageous evacuation units (DEU) is introduced to represent the pick-up locations selected for bus allocation with limited resource. The binary integer linear programming (BILP) named risk-based bus schedule model incorporated with DEU notion can provide a reference for resource allocation in stage of both evacuation planning and operation for transit-dependent population. The proposed model structure can effectively capture the changes of evacuation risk among pick-up locations over time to realize real-time bus schedule. Numerical experiments are conducted using the transportation network of the city of Xi’an, China, to test the performance of the model. The applicability and comparison of different bus evacuation models are also discussed in this paper. This research provides insights into dealing with disaster dynamics and time-varying supply conditions in realistic bus-based no-notice evacuation operations.

A cost-minimization model for bus fleet allocation featuring the tactical generation of short-turning and interlining options

Urban public transport operations in peak periods are characterized by highly uneven demand distributions and scarcity of resources. In this work, we propose a rule-based method for systematically generating and integrating alternative lining options, such as short-turning and interlining lines, into the frequency and resource allocation problem by considering the dual objective of (a) reducing passenger waiting times at stops and (b) reducing operational costs. The bus allocation problem for existing and short-turning/interlining lines is modeled as a combinatorial, constrained and multi-objective optimization problem that has an exponential computational complexity and a large set of decision variables due to the additional set of short-turning/interlining options. This constrained optimization problem is approximated with an unconstrained one with the use of exterior point penalties and is solved with a Genetic Algorithm (GA) meta-heuristic. The modeling approach is applied to the bus network of The Hague with the use of General Transit Feed Specification (GTFS) data and Automated Fare Collection (AFC) data from 24 weekdays. Sensitivity analysis results demonstrate a significant reduction potential in passenger waiting time and operational costs with the addition of only a few short-turning and interlining options.

Bus Allocation for Voltage Stabilization in Multi-Machine Power System Using GWO

Today, power systems are being operated under greatly stressed conditions due to rapidly growing demand for electrical energy, penetration of renewable energy sources, large seasonal load variations, and operation in competitive energy market conditions. The main part of this work involves achieving complete system observability and improving voltage stability level simultaneously by placing a minimal number of PMUs. Initially, weak buses (very sensitive to smaller reactive power variations) are identified by using Fast Voltage Stability Index (FVSI) calculation. In this work, the reliability of observing the weak buses is purposely maximized. Suppose any sudden voltage instability problem occurs on the weak bus. In that case,PMUs can immediately communicate to the operator and prevent the outage of the weak bus, even if one of the PMUs fails. PMUs can maintain the FVSI value of the critical lines, not exceeding their maximum limit, by initiating the remedial actions scheme, such as smart islanding, controlling the transformer tap settings, coordinating between automatic corrective devices, etc. In this system, the positioning of PMUs on poor buses should be favoured over other buses. Improved voltage reliability can be accomplished by effective and accurate control of critical lines and vulnerable buses. This is done by a small rise in the amount of PMUs relative to previous results.

Research on SOC Test Bus Allocation Algorithm under the Constraint of Temperature

The main concern is over rising temperature during the testing of complex system-onchip (SOC), this paper studies SOC wrapper and test access mechanism (TAM), and proposes an improved algorithm of TAM assignment under the constraints of temperature. The algorithm uses temperature superposition method and adds compression process. This algorithm can find the test structure that uses shorter test time.

Optimization of bus allocation to depots by minimizing dead kilometers

SummaryIn metropolitan cities, public transportation service plays a vital role in mobility of people, and it has to introduce new routes more frequently due to the fast development of the city in terms of population growth and city size. Whenever there is introduction of new route or increase in frequency of buses, the non‐revenue kilometers covered by the buses increases as depot and route starting/ending points are at different places. This non‐revenue kilometers or dead kilometers depends on the distance between depot and route starting point/ending point. The dead kilometers not only results in revenue loss but also results in an increase in the operating cost because of the extra kilometers covered by buses. Reduction of dead kilometers is necessary for the economic growth of the public transportation system. Therefore, in this study, the attention is focused on minimizing dead kilometers by optimizing allocation of buses to depots depending upon the shortest distance between depot and route starting/ending points. We consider also depot capacity and time period of operation during allocation of buses to ensure parking safety and proper maintenance of buses. Mathematical model is developed considering the aforementioned parameters, which is a mixed integer program, and applied to Bangalore Metropolitan Transport Corporation (BMTC) routes operating presently in order to obtain optimal bus allocation to depots. Database for dead kilometers of depots in BMTC for all the schedules are generated using the Form‐4 (trip sheet) of each schedule to analyze depot‐wise and division‐wise dead kilometers. This study also suggests alternative locations where depots can be located to reduce dead kilometers. Copyright © 2015 John Wiley & Sons, Ltd.

Application of Linear Programming in Modeling the Allocation of Buses to Routes in a Transport Service Authority

The objective of the study was to allocate the available buses of the transport authority to the authority's service intra and inter states routes in a manner that will yield optimum profit, taking all the constraints into consideration. The problem was modeled using Linear Programming (LP) and the TORA - computer software result yielded a maximum objective value of N897, 214 per day after 20 iterations, which is a better result compared to the current intuitive schedule by the authority that yields N766, 046 per day.

Pick‐up locations and bus allocation for transit‐based evacuation planning with demand uncertainty

SUMMARYThis paper develops a decision‐support model for transit‐based evacuation planning under demand uncertainty. Demand uncertainty refers to the uncertainty associated with the number of transit‐dependent evacuees. A robust optimization model is proposed to determine the optimal pick‐up points for evacuees to assemble, and allocate available buses to transport the assembled evacuees between the pick‐up locations and different public shelters. The model is formulated as a mixed‐integer linear program and is solved via a cutting plane scheme. The numerical example based on the Sioux Falls network demonstrates that the robust plan yields lower total evacuation time and is reliable in serving the realized evacuee demand. Copyright © 2012 John Wiley & Sons, Ltd.