Total Delay Time Research Articles

Delay or travel time information? The impact of advanced traveler information systems on drivers’ behavior before freeway work zones

Peak travel times contribute to congestion formation at freeway work zones. Advanced Traveler Information Systems (ATIS) can inform drivers in real-time about the delays and travel times en-route and can provide information about an alternative route to a destination. Different ATIS display strategies are available; however, road authorities lack insights into how time display methods and sign characteristics influence the driving behavior (decelerations, lateral position), the drivers’ attention allocation ability and the subsequent route choice before the freeway diversion. A driving simulator study was conducted with 80 drivers in the State of Qatar to investigate drivers' behavior and voluntary route choices when encountering total travel time (default setting) or delay time updates for two freeway routes on Variable Message Signs (VMS) and Graphical Route Information Panels (GRIP). The GRIPs are a graphical alternative to conventional VMS that can provide drivers with a visual map of the most direct route or an alternative less congested route to a destination using different color schemes. The time difference ratio between the two routes was kept constant to compare the effectiveness of the information designs and investigate the drivers’ attention towards the signs with an eye-tracker. The results showed that the display of zero delays for a detour did influence 74–83% of the drivers to take the alternative route when being displayed on a VMS and a GRIP with free flow attribute framing. When displaying equal total travel times, the GRIP did influence 25% more drivers to follow the alternative route than the VMS. Generally, displaying zero delays for the alternative route resulted in an efficient attention allocation to the first ATIS location and fewer mean decelerations before the repeated ATIS location nearing the diversion. Road authorities are advised to activate the display of delay times to support efficient route choices among freeway drivers.

An Improved Genetic Algorithm-Based Traffic Scheduling Model for Airport Terminal Areas

This paper takes the airport terminal area as the main research content and combines genetic algorithm with airport terminal area analysis theory to analyze and study the traffic scheduling in the airport terminal area. Based on the study of traditional traffic scheduling techniques and key techniques of genetic algorithms, this paper participates in the actual project of genetic algorithm-based traffic scheduling, analyzes the requirements of the project, focuses on the design and implementation of the traffic scheduling algorithm module in the genetic algorithm-based traffic scheduling system, and conducts further research on the pathfinding by constraints submodule. In this paper, the flight approach and departure sequencing problem and runway allocation problem are the main research objects. The dynamic optimal scheduling model of flight approach and departure is established by considering the interests and demands of airlines and airports, and a new scheduling algorithm is proposed. In this paper, a brief introduction to the airport terminal area is given, and the feasibility of the approach/departure optimal scheduling is introduced from the perspective of airlines with a long-range parallel two-runway airport as the research background. Secondly, through the analysis of the flight approach and departure process and the study of the approach and departure cooperative optimization strategy, a single-runway flight approach and departure traffic scheduling model under the joint sequencing strategy is established with the optimization objective of minimizing the total flight delay time, and the model is solved by using the sliding time window algorithm. Then, based on the single-runway scheduling model, a multirunway multiobjective flight optimal scheduling model is established with the objectives of minimizing total delay time, increasing runway throughput per unit time and fairness of flight delay time allocation, and a dynamic algorithm (STW-GA) combining sliding time window algorithm and dual-structured chromosome genetic algorithm is proposed to solve the model.

Urban traffic light scheduling for pedestrian–vehicle mixed-flow networks using discrete sine–cosine algorithm and its variants

This study addresses the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks. A macroscopic model, which strikes an appropriate balance between pedestrians’ needs and vehicle drivers’ needs, is employed to describe the traffic light scheduling problem in a scheduling framework. The objective of this problem is to minimize the total network-wise delay time of vehicles and pedestrians within a given finite-time window, which is crucial to avoid traffic congestion in urban road networks. To achieve this objective, the present study first uses a well-known optimization solver called GUROBI to obtain the optimal solution by converting the problem into mixed-integer linear programming. The obtained results indicate the computational inefficiency of the solver for large network sizes. To overcome this computational inefficiency, three novel metaheuristic methods based on the sine–cosine algorithm are proposed. These methods are denoted by discrete sine–cosine algorithm, discrete sine–cosine algorithm with local search operator, and discrete sine–cosine algorithm with local search operator and memory utilization inspired by harmony search. Each of these methods is developed hierarchically by taking the advantages of previously developed method(s) in terms of a better search process to provide more accurate solutions and a better convergence rate. To validate all these proposed metaheuristics, extensive computational experiments are carried out using the real traffic infrastructure of Singapore. Moreover, various performance measures such as statistical optimization results, relative percentage deviation, computational time, statistical analysis, and convergence behavior analysis have been employed to evaluate the performance of algorithms. The comparison of the proposed SCA variants is done with GUROBI solver and other metaheuristics namely, harmony search, firefly algorithm, bat algorithm, artificial bee colony, genetic algorithm, salp swarm algorithm, and harris hawks optimization. Overall comparison analysis concludes that the proposed methods are very efficient to solve the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks with different network sizes and prediction time horizons.

Numerical Investigation of the Ignition Delay Time of Kerosene Premixed Combustion in an SI Engine

SI engines are installed widely in small aircrafts as they have good fuel economy. Currently, these SI engines are fueled with gasoline, although their safety can be improved if kerosene is used. However, the combustion performance of kerosene cannot fulfil the requirements due to the differences in physicochemical properties. This study investigates the ignition delay time of kerosene at a pressure range of 15–35 bar and a temperature range of 600–1000 K. A detailed chemical reaction mechanism is employed for the premixed combustion process. Under the initial conditions of 1000 K and 35 bar, with an equivalence ratio of 1, the total ignition delay time of kerosene is 0.401 ms. The NTC range of kerosene is determined as roughly 750–920 K. Subsequently, the chemical reaction paths with an equivalence ratio of 0.8, 1, and 1.2 and an initial pressure of 15, 20, and 25 bar were analyzed. The rate-determined elementary reactions were obtained based on a sensitivity analysis. The difference between kerosene and gasoline are also compared, and the rate-determining reactions that affect the ignition of kerosene and gasoline are discussed. The results of this study can provide a reference for the combustion performance improvement and knock suppression of SI engines fueled with kerosene.

Анализ эффективности способов приоритета автобусам при проезде перекрестков с применением адаптивного управления светофорами

In recent years in the cities of the Russian Federation, within the framework of the national project “Safe and high-quality roads” the concept of “priority development of public transport” has been actively implemented and Intelligent transport systems have been created. The article discusses ways to prioritize public transport when passing through intersections using adaptive traffic light control as a possible alternative to a lane for route vehicles. The aim of the study is to reduce the loss of time for road users by determining the best ways to prioritize buses when passing regulated intersections, taking into account the number of passengers in public transport and the loss of time for all traffic participants. During the study, field observation methods, mathematical and simulation modeling were used. The account of the moments of the approach of buses to the regulated intersection and the analysis of the loss of time for buses when passing through the section on the central street of Tyumen was carried out. The scientific novelty lies in establishing the dependencies of the average speed of the bus on the approach to the intersection from the moment until the end of the traffic signal, the delay time of the buses on the number and moment of stopping the buses when passing the intersection. With the help of simulation micromodeling, the dependence of the delay time of buses and cars on traffic intensity was established for five options for priority to public transport. Two variants of priority for buses when passing through intersections are based on the methods of adaptive control of traffic lights and the algorithms “stage recall” and “green extension”. The use of a dedicated lane and the “stage recall” method at high traffic intensity reduces the delay time for buses by 94%, for personal vehicles – increases by 85%. At low traffic intensity, the delay time changes by 11 and 6%, respectively, for buses and cars. The use of the “green extension” method allows to reduce the total delay time by 3.2% with a high traffic volume (1900 veh/h) and a small number of buses (40 veh/h) and has a greater effect than the lane for fixed-route vehicles (deterioration by 148%). To determine the most efficient priority method, it is proposed to take into account the number of passengers in public transport. The dependence of the total delay time of all traffic participants on the traffic intensity of cars and buses with different priority methods has been established. This approach allows us to further determine the area of rational application of priority methods for public transport with the target function of reducing the loss of time for all traffic participants.

Mobile Fog Computing by Using SDN/NFV on 5G Edge Nodes

Fog computing provides quality of service for cloud infrastructure. As the data computation intensifies, edge computing becomes difficult. Therefore, mobile fog computing is used for reducing traffic and the time for data computation in the network. In previous studies, software-defined networking (SDN) and network functions virtualization (NFV) were used separately in edge computing. Current industrial and academic research is tackling to integrate SDN and NFV in different environments to address the challenges in performance, reliability, and scalability. SDN/NFV is still in development. The traditional Internet of things (IoT) data analysis system is only based on a linear and time-variant system that needs an IoT data system with a high-precision model. This paper proposes a combined architecture of SDN and NFV on an edge node server for IoT devices to reduce the computational complexity in cloud-based fog computing. SDN provides a generalization structure of the forwarding plane, which is separated from the control plane. Meanwhile, NFV concentrates on virtualization by combining the forwarding model with virtual network functions (VNFs) as a single or chain of VNFs, which leads to interoperability and consistency. The orchestrator layer in the proposed software-defined NFV is responsible for handling real-time tasks by using an edge node server through the SDN controller via four actions: task creation, modification, operation, and completion. Our proposed architecture is simulated on the EstiNet simulator, and total time delay, reliability, and satisfaction are used as evaluation parameters. The simulation results are compared with the results of existing architectures, such as software-defined unified virtual monitoring function and ASTP, to analyze the performance of the proposed architecture. The analysis results indicate that our proposed architecture achieves better performance in terms of total time delay (1800 s for 200 IoT devices), reliability (90%), and satisfaction (90%).

The Impact of Width Downscaling on the High-Frequency Characteristics of InGaAs Nanowire FETs

This work demonstrates the high-frequency characteristics of In0.53Ga0.47As nanowire with scaled wire width by implementing TCAD simulations. The physical models and correlated parameters have been calibrated to the experiments in the literature 19. As the width of the nanowire is scaled to 10 nm, the electron density peaks are no longer located close to the oxide/semiconductor interface. Instead, the peaks merge and volume inversion effects appear due to the strong quantum confinement. The volume inversion effects lead to higher cut-off frequency due to the reduced total transport delay time. To have a better understanding of this phenomena, the high-frequency properties of nanowire were quantified with the assistance of small-signal analysis and delay time analysis using TCAD. It is found that the channel charging delay increases with narrower wire width due to the raise of source/drain resistance. Regarding the extrinsic and intrinsic delay, they increase with smaller wire width and drop at width of 10 nm due to the volume inversion effects. Electron distribution which aims to clarify the abovementioned observation is also plotted.

Robust Adaptive Observer-Based Predictive Control for a Non-Linear Delayed Bilateral Teleoperation System

In this paper, a new robust adaptive nonlinear teleoperation system using an improved extended active observer (IEAOB), adaptive Smith predictor (ASP) and sliding mode control is developed to address the time delay in the communication channels and the nonlinear robot model uncertainties. Firstly, an ASP based on Padé approximation and active observer is designed to compensate for the time delay effect. Specifically, the total network time delay is modelled by Padé approximation, and then an active observer is deployed to estimate the time delay. To ensure the time-varying delay effect is completely suppressed, a sliding mode control algorithm is further developed. Meanwhile, the IEAOB is utilized at both the master and slave sides to deal with the nonlinear model uncertainties, it can provide an accurate robot inertial parameters and friction estimation as well as external force estimation without a structured force model in the presence of disturbances. Finally, the stability of the designed teleoperation system is theoretically studied and the system effectiveness is demonstrated by applying it to a pair of Phantom Omni haptic devices connected via a communication channel with time-varying delays.

Experimental and modeling study of the autoignition characteristics of gasoline/hydrogenated catalytic biodiesel blends over low-to-intermediate temperature

Gasoline/Hydrogenated catalytic biodiesel blending fuel is a prospective alternative fuel for gasoline compression ignition (GCI) engine which has the potential to solve the ignition difficulties of GCI especially at low load conditions. However, the autoignition behavior of gasoline/hydrogenated catalytic biodiesel blends is unknown. In this study, the ignition delay times of gasoline/hydrogenated catalytic biodiesel (HCB) blends were measured in a heated rapid compression machine over a wide range of conditions (the temperature range of 655–870 K, pressures of 10 and 15 bar, equivalence ratios of 0.5–1.5). Experimental results show that the measured ignition delay time of the gasoline/HCB blends exhibits negative-temperature coefficient behaviors under the investigated conditions. The total ignition delay times diminish with rising pressure, equivalence ratio and HCB ratio in the blends. The simulation results of ignition delay times for gasoline/HCB blends using a detailed mechanism are compared with experimental data. The detailed model can predict the trends of ignition delay time well during low-to-intermediate temperature region. Finally, kinetic analyses were performed under different compressed pressure, temperature, equivalence ratios and blending ratios. The dominating reactions and decisive factors of ignition are explored to gain a deeper understanding of the ignition characteristic of gasoline/HCB blends. It is found that the autoignition of fuel is completely caused by HCB related reactions at low temperature, which explains why the blending of HCB can expand the working condition downward from the perspective of chemical kinetic.

An intelligent tunnel firefighting system and small-scale demonstration

Disastrous fire event in the confined tunnel is a fatal hazard, threatening the lives of trapped people and firefighters. Considering the rapid development of fire and the complex environment of tunnels, an accurate and timely fire identification system is in urgent need for guiding the evacuation, rescue, and firefighting actions. This study proposes an intelligent system and digital twin composed of four main components to collect, manage, process and visualize the tunnel fire information. As demonstrated in a laboratory-scale tunnel model, the AI model is trained with a large numerical database to successfully identify the fire size and location. The whole system is assessed in terms of accuracy, timeliness and robustness. The AI model attained an overall accuracy of 98% in predicting the tunnel fire scenarios. The total time delay is around 1 s from the on-site measurement of temperature to the final display of the tunnel fire scenario on a remote user interface. Moreover, the system is robust enough to predict fire, even if part of the temperature sensors is failed or destroyed by fire. The proposed intelligent system will be a valuable step for smart firefighting from the concept to practice.

Efficient Small-Batch Verification and Identification Scheme With Invalid Signatures in VANETs

Vehicular Ad hoc networks (VANETs) plays an important role in reducing casualties, optimizing traffic distribution and improving traffic efficiency. Nevertheless, with the rapid growth of vehicles, the existing VANETs computing capacity will not be able to guarantee the service quality due to inefficient signature verification and identification. In this paper a small-batch verification and identification scheme is presented to speed up the signature verification and the invalid signature identification. In the proposed scheme, according to the signature number and the historical signature error rate, we first build an optimization model, which transforms the problem how to divide a batch of signatures into several appropriate small-batches into an integer programming problem, and then stores the optimal batch solution table in roadside units (RSUs). Moreover, in view of the current task, RSU can automatically divide a batch of signatures into several appropriate small-batches. After then, any invalid signature in these small-batches can be quickly identified by using of the exponentiation method. Finally, by comparison with the existing schemes, the total time delay is reduced to 50% in optimal circumstances. Simulation results show that the proposed scheme can achieve fast signature verification and invalid signature identification.

PROPOSALS FOR THE ESTABLISHMENT OF A REMOTE DIAGNOSTIC DEVICE FOR CONTROL OF THE TECHNICAL CONDITION OF OVERHEAD TRANSMISSION LINES 6-35 kV

Today, the main activity of Ukrainian electricity companies is to meet the ever-growing needs of Ukrainian consumers in quality and reliable electricity supply. For this purpose, constant work is carried out on the reconstruction of electrical networks and construction of new energy facilities on the basis of a new regulatory framework adapted to world standards. The article proposes to determine the location of damage to a branched power line with several power sources, namely, to create a device for diagnosing power lines. The device for diagnosing power lines is designed to isolate the damaged branch in the branched overhead power lines, and measure the distance to the place of damage. The operation of the device is that the meters of inhomogeneity of cables and lines measure the delay time of the probing pulses reflected from the place of damage of the branched power lines, from the beginning of the main line and from its end. Next, the total delay time of the probing pulses reflected from the place of damage of the branched transmission line is compared with the time of passage of the probing pulses on the main transmission line, if the delay time is equal, the damage is on the main transmission line. branch. Then, based on the obtained measurements, the exact location of the damage on the power line is calculated. The technical result is to increase the accuracy of determining the location of damage on branched transmission lines with multilateral power supply by attracting a priori information (known all the inhomogeneities of a good transmission line) and the location of the transmission line inhomogeneity (branching) and measuring . The proposed approach does not involve the use of specific instruments for measuring current and voltage values, but is based only on measuring the delay time of probing pulses using line inhomogeneity meters. Determining the importance of constant diagnostics of the technical condition of power lines in the article shows one of the real ways to create a remote diagnostic device that can be implemented on modern elements and computer technology.

Macroscopic Real-Time Timetable Rescheduling Approach for High-Speed Railway under Complete Blockages Using a Three-Stage Algorithm

The rescheduling of train timetables under a complete blockage is a challenging process, which is more difficult when timetables contain lots of trains. In this paper, a mixed integer linear programming (MILP) model is formulated to solve the problem, following the rescheduling strategy that blocked trains wait inside the stations during the disruption. When the exact end time of the disruption is known, trains at stations downstream of the blocked station can depart early. The model aims at minimizing the total delay time and the total number of delayed trains under the constraints of station capacities, activity time, overtaking rules, and rescheduling strategies. Because there are too many variables and constraints of the MILP model to be solved, a three-stage algorithm is designed to speed up the solution. Experiments are carried out on the Beijing–Guangzhou high-speed railway line from Chibibei to Guangzhounan. The original timetable contains 162 trains, including 29 cross-line trains and 133 local trains. The simulation results show that our model can handle the optimization task of the timetable rescheduling problem very well. Compared with the one-stage algorithm, the three-stage algorithm is proved to greatly improve the solving speed of the model. All instances can get a better optimized disposition timetable within 450 to 600 s, which is acceptable for practical use.

Prehospital Delay and its Impact on 30 days Outcomes in patients with Myocardial Infarction in Alshaab teaching hospital, Khartoum November2020-April 2021

BackgroundIschemic heart diseases are the most common cause of death worldwide, Prehospital delay associated with increased mortality and poorer outcome. This study aimed to determine whether particular patient's characteristics are associated with delays in seeking care, it also determined the impact of the delay on in-hospital outcomes, 30 days’ outcomes, and the cardiac functional status for patients after 30 days of their disease. MethodsThis prospective 30 days’ cohort study was conducted in Al shaab tertiary hospital in Khartoum. All patients diagnosed with myocardial infarction from 13th of January to 13th of February 2021 were included. Sociodemographic, clinical features, and health seeking behavior were collected using structured questionnaire, Patients were followed up by telephone to collect in-hospital and 30 days’ outcomes data. The Duke Activity Status Index (DASI) was used to assess the cardiac functional status after 30 days; Wilcoxon rank-sum test, Chi-square test of independence and Fisher's exact test were used to find if there is difference between groups categorized according to pre-hospital delay to tertiary hospital (< 6 hours or ≥ 6 hours), and to find the difference in the prognosis of patients according to pre-hospital delay time to hospital. Results98 patients were enrolled. The mean age for the participants was (58.6 ± 10.3) years. The mean total pre-hospital delay time was (11.0 ± 6.3) hours and the mean time patients took to decide that their condition requires medical attention was (5.6 ± 5.9) hours. The mean time for first medical interactions patients had was (6.9 ± 6.0) hours. Patients misperception of symptoms significantly contributed to prolonged pre-hospital times (p=0.002). Prolonged referral time led to prolonged prehospital time as the mean time for making the referrals was (2.7 ± 3.1) hours (p=0.005). STEMI patients tended to have earlier first medical interactions than patients diagnosed with Non-STEMI. Hypertensive patients had prolonged first medical interactions (p=0.016). In addition, prolonged time to first medical interaction led to a significant decrease in the cardiac functional status index after 30 days of admission as measured by the Duke Activity Status Index (DASI). ConclusionSome modifiable factors contribute to longer pre-hospital delay of myocardial infarction patients, resulting in decreased cardiac functional status after one month, patient's awareness about cardiovascular disease and improved referral pathway of the existing health care system may reduce the delay.

Dynamic Task Scheduling in Multiprocessor Real Time Systems Using Genetic Algorithms

The objective of the scheduling algorithm is to dynamically schedule as many tasks as possible such that each task meets its execution deadline while minimizing the total delay time of all of the tasks. The problem of scheduling of real-time tasks in multiprocessor systems is to determine when and on which processor a given task executes. In this paper we suggest a genetic algorithm for dynamic scheduling of real time tasks in multiprocessors system.The algorithm based on the use of a fixed size chromosome and repeatedly applying specific crossover (single point or double point) and mutation procedures with variable mutation rates (0.05 – 0.1) until all tasks are successfully scheduled.

Research on Scheduling Problem of Manufacturing/remanufacturing Hybrid Systems

Remanufacturing plays a significant effect on saving social resources, developing green economy and reducing enterprise cost. Aiming at a production scheduling problem in manufacturing/remanufacturing hybrid systems we investigated, a multi-objective optimal scheduling model is built. The goals of optimization are to minimize total equipment idle time, total delivery delay and total setup time which are consistent with actual needs of the enterprise. An improved NSGA-II is adopted to increase the population diversity and improve the search performance. The similarity degree S is employed to evaluate the diversity of population in this paper. Crossover and mutation operations are adjusted adaptively based on S. This algorithm is applied to an engine manufacturing enterprise compared with the original genetic algorithm. The analysis of experimental results shows that the way in this paper has certain superiority.

Computation Offloading and Shunting Scheme in Wireless Wireline Internetwork

In the multi-server multi-access edge computing (MEC) system, the computing capacity could be insufficient and the computing resource could be over-utilized when an excessive number of computing tasks are offloaded for execution. To alleviate the excessive burden in the multi-server MEC system and tap the underutilized resources of wired edge devices, which are called edge computers in this paper, we investigate the computation offloading and shunting scheme in the wireless wireline internetwork. We formulate an optimization problem to minimize the average total time delay and use the deep reinforcement learning method to obtain the optimal shunting policy. By sensing the state of the wireless wireline internetwork, the proposed scheme can adaptively adjust the shunting ratio and utilize the resources of MEC servers and edge computers intelligently and jointly. Finally, we evaluate the computational complexity and validate the convergence property of this scheme. The extensive simulation results demonstrate that this scheme can improve the resource productivity and reduce the average total time delay.

Efficient heuristics to minimize the total tardiness of chemotherapy drug production and delivery

This study considers the production of chemotherapy drugs for cancer treatment. An important factor determining the quality of service of chemotherapy treatment is the time the patient must wait to receive his or her injection of the chemotherapy drug. Chemotherapy production and delivery are modeled as a production scheduling problem combined with a vehicle routing problem. The scheduling problem is a three-stage hybrid flow shop scheduling problem, and the routing problem is a variant of the multi-trip vehicle routing problem with due dates. The objective function is the minimization of the total time delay for chemotherapy treatment. To solve this problem, we propose several heuristic algorithms to provide quality solutions within reasonable computation times. Computational experiments are used to compare the performance of the heuristics applied to real data-based random instances.

Berth Allocation Model for Container Terminal using Genetic Algorithm Technique: Case of Apapa Wharf, Lagos, Nigeria

The Nigeria ports plays a vital role in socio-economic growth by being a cheap mode of conveying shipments for importation and exportation. The number of vessels coming into the Nigerian ports every year is on the average of about 4,900. A well flourishing and efficient ports and cargo management will in no doubt put a developing economy such as Nigeria in a leading pedestal with developed nations. Thus, stakeholders in container terminals are concerned about discharging containers as fast as possible, with the purpose of saving terminal costs. This study is driven to minimize the time being used up by ships in container terminal using genetic algorithm (GA) and thus attain maximum efficiency. The limited berth space in the wharf lead to berth allocation problem (BAP) and an optimal solution is required. Moreover, high berth occupancy results in congestion where vessels are queuing to be served. This leads to high turn-around time and results in bad service for the container terminal. The aim of this study is to develop and implement a genetic algorithm based model for berth allocation (i.e. GAMBA) with the view to minimize the total delay times of vessels at container terminals. A study of the operations in Apapa wharf was done with the view to understand the berth allocation process vis-à-vis the challenges therein. The relevant parameters required for berth allocation were identified and GAMBA was developed using the identified parameters. GAMBA was implemented using real life data collected from the container terminal, Apapa, Lagos, Nigeria. The results showed that increasing the quay length by 250m has a very similar outcome on the container port’s efficiency as reducing the proportion of increasing handling time by 0.0025 h/m. This revealed that the outcome on the container port’s efficiency by increasing the quayside length was the same as reducing the proportion of increasing management time. Based on these results, the optimized allocation of container storage and the automation of the handling process can be proposed as cheaper alternatives to construction and development of the containers port in relation to increasing the productivity of the port.

Identification of Factors to Improve Public Transit Services (A Case Study of Prithvi Chowk to Talchowk Section of Prithivi Highway)

This research presents studies on a segment of highway to determine the quantitative factors that inuence transit services. Travel time and delay study is one of the method to determine quantitative factors. Tour time is described as the average period of time required to journey from one region to some other. Total departure time consists of gadgets which include total working time, places and general delay time. The examine section was done in Prithvi chowk to Tal chowk of Prithvi Highway which is turned to be 12.5 km long. Additionally, it has been found that the principle variables affecting travel time are: postpone time because of forestall selecting and choosing up passengers, bus model and bus size.32 trips public transport carrier and a 10 trips non-public automobile journey have been held during peak hours. Models are developed the use of SPSS software to become aware of the relationship between the causes of delays and the overall-time delays. Travel time and learning delays can help reduce the number of private vehicles operating and increase the number of public vehicles in order to reduce congestion and improve the e efficiency of the public transport system. It turned into determined that there was a full-size distinction in tour time among the use of the public transit services and the car.