scholarly journals Optimizing Customized Transit Service considering Stochastic Bus Arrival Time

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Qian Sun ◽  
Steven Chien ◽  
Dawei Hu ◽  
Xiqiong Chen
Keyword(s):  
Probabilistic Model ◽  
Arrival Time ◽  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Urban Setting ◽  
Model Parameters ◽  
Service Reliability ◽  
Total Cost ◽  
Transit Service ◽  
Decision Variables

The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.

scholarly journals A Dynamic Short-Turning Bus Control for Uncertain Demand

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hu Zhang ◽  
Shuzhi Zhao ◽  
Huasheng Liu ◽  
Jin Li
Keyword(s):  
Real Time ◽  
Arrival Time ◽  
Uncertain Demand ◽  
Service Reliability ◽  
Real Time Control ◽  
Dynamic Approach ◽  
Short Term ◽  
Total Cost ◽  
Transit Service ◽  
Time Control

This paper formulates a dynamic approach for real-time bus control in uncertain demand. This dynamic approach aims to save the total cost for passengers and operators, while improving transit service reliability. An unfixed rolling horizon was implemented to choose the best dynamic approach. Real-time control predicts two discrete variables (arrival time and bus position) and determines the space-time point of buses. Furthermore, controlled actions include stop skipping and bus holding. The holding time starts when a bus serves a station and depends on previous intervals of passenger boarding and alighting at the station. The stop skipping action allows a bus to skip not only stations with a short-turning exception, but also stations with low demand for boarding that have been alighted in the short-turning segment. Stop skipping and bus holding actions for short-turning service both decrease the travel time of served passengers and the running time of buses, thus improving transit service reliability. A genetic algorithm was applied to solve the problem and the validity of the proposed dynamic approach was tested with four different scenarios. The result of these tests shows that a dynamic short-term bus control can significantly reduce total cost and improve transit service reliability.

Abstract TP241: Race-ethnic Differentials Time and Acute Stroke Emergency Presentation: The ASPIRE Study

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Bernadette Boden-Albala ◽  
Dorothy F Edwards ◽  
Jeffrey J Wing ◽  
Shauna S Clair ◽  
Stephen Fernandez ◽  
...  
Keyword(s):  
Black Women ◽  
Acute Stroke ◽  
Intervention Program ◽  
Arrival Time ◽  
Proportional Hazards ◽  
Ethnic Disparities ◽  
Cox Proportional Hazards ◽  
Urban Setting ◽  
Intervention Period ◽  
Post Intervention

BACKGROUND: There is sparse data about the nature of race-ethnic disparities in the acute stroke setting including differentials in stroke preparedness. The aim of this analysis was to explore race-ethnic differentials in time to arrival for acute stroke in a racial and ethnically diverse urban setting. METHODS: ASPIRE is a multi-dimensional intervention program (community, hospital, and EMS) for acute stroke preparedness targeted to increase IV tPA utilization in underserved black communities in the DC metro area. We prospectively identified stroke admissions and EMS utilization including acute stroke arrival time parameters for the 6 month pre and post intervention periods. Cox proportional hazards models were used to examine predictors of arrival time. Proportionality of the hazards was checked. RESULTS: In the 6 month pre-intervention period, data was collected on 943 stroke cases; 53% female; 74% black; mean age 67 yrs. Of the subjects from the pre-intervention period with arrival times less than 48 hrs, the median arrival time to the emergency department (ED) was 9 hours; 20% presented under 3 hours. In multivariable Cox PH models, subjects were 38% more likely to arrive earlier if they had arrived by EMS (HR: 1.38, 95%CI: 1.21-1.58). Black subjects were 25% less likely to arrive earlier (HR: 0.75, 95%CI: 0.60-0.93), but this effect was dampened over time (p=0.03). The model included the interaction between black race and time and adjusted for insurance status, risk factors (hypertension and diabetes), gender, age and prior stroke. Ina gender by race analysis, there was a trend towards black women being less likely to arrive earlier to the ED (HR 0.78, 95% CI 0.6 -1.0). However, overall, there was no race-ethnic interaction with arrival by EMS. CONCLUSIONS: Contrary to the perceived perception by the community suggesting there is a disparity in EMS utilization by the black DC community, we found no overall significant racial difference in EMS utilization for acute stroke. While there was a trend towards delayed overall arrival in black females, this was independent of EMS utilization.

scholarly journals Recurrence time distribution and temporal clustering properties of a cellular automaton modelling landslide events

2013 ◽  
Vol 20 (6) ◽  
pp. 1071-1078 ◽  
Author(s):  
E. Piegari ◽  
R. Di Maio ◽  
A. Avella
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Cellular Automaton ◽  
Time Window ◽  
Fano Factor ◽  
Recent Analysis ◽  
Recurrence Time ◽  
Model Parameters ◽  
Reasonable Prediction ◽  
First Time

Abstract. Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

scholarly journals Research contributions to managing transit service reliability

1987 ◽  
Vol 21 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Mark Abkowitz ◽  
John Tozzi
Keyword(s):  
Service Reliability ◽  
Transit Service

scholarly journals Model and Algorithm of Two-Stage Distribution Location Routing with Hard Time Window for City Cold-Chain Logistics

2020 ◽  
Vol 10 (7) ◽  
pp. 2564
Author(s):  
Liying Yan ◽  
Manel Grifoll ◽  
Pengjun Zheng
Keyword(s):  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Distribution Networks ◽  
Cold Chain ◽  
Two Stage ◽  
Logistics Network ◽  
Stage Distribution ◽  
Hard Time ◽  
Location Routing ◽  
Logistics Enterprises

Taking cold-chain logistics as the research background and combining with the overall optimisation of logistics distribution networks, we develop two-stage distribution location-routing model with the minimum total cost as the objective function and varying vehicle capacity in different delivery stages. A hybrid genetic algorithm is designed based on coupling and collaboration of the two-stage routing and transfer stations. The validity and feasibility of the model and algorithm are verified by conducting a randomly generated test. The optimal solutions for different objective functions of two-stage distribution location-routing are compared and analysed. Results turn out that for different distribution objectives, different distribution schemes should be employed. Finally, we compare the two-stage distribution location-routing to single-stage vehicle routing problems. It is found that a two-stage distribution location-routing system is feasible and effective for the cold-chain logistics network, and can decrease distribution cost for cold-chain logistics enterprises.

scholarly journals Research on Logistics Distribution Path Planning Based on Genetic Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032013
Author(s):  
Shaokun Liu
Keyword(s):  
Genetic Algorithm ◽  
Simulation Experiment ◽  
Time Window ◽  
Planning Model ◽  
Genetic Operators ◽  
Intelligent Algorithm ◽  
Total Cost ◽  
Logistics Distribution ◽  
Survey Results ◽  
Distribution Cost

Abstract In this paper, SF express company Jinzhou Guta District Pinganli business point as an example, to investigate its distribution, statistical analysis of the survey results, summed up the problems in logistics and distribution. Through the systematic study of the problem, a planning model with time window and with the objective of minimizing the total cost of distribution is established. At the same time, an intelligent algorithm for distribution path optimization - Genetic Algorithm (GA) is designed. Genetic algorithm is used to design chromosome coding methods and genetic operators for solving the planning model with the objective of minimizing the total cost of distribution. Finally, the simulation experiment is carried out. MATLAB software is used to solve the distribution route and the total driving distance of vehicles, and the distribution route with the goal of minimizing the total distribution cost is obtained.

scholarly journals Inventory and preservation investment for deteriorating system with stock-dependent demand and partial backlogged shortages

2020 ◽  
pp. 38-38
Author(s):  
Nita Shah ◽  
Kavita Rabari ◽  
Ekta Patel
Keyword(s):  
Sensitivity Analysis ◽  
Total Cost ◽  
Deterioration Rate ◽  
Preservation Technology ◽  
Decision Variables ◽  
Stock Dependent Demand ◽  
Rate Controlled ◽  
Financial Losses ◽  
Constant Deterioration ◽  
Dependent Demand

Our model deals with the stock-dependent demand as exhibiting huge volume of commodities leads to more costumers and augment the trading of the goods. As some goods like vegetables, fruits, medicines deteriorate after a period of time, resulting in economical and financial losses, we took this factor into consideration and included a constant deterioration rate, controlled by suitable preservation technologies. Preservation technology investments are made for the valuable business as it helps to decrease the rate of deterioration. Our model allows shortages, and back-ordering is permissible to manage the loss that occurs due to perishable objects and shortages. The objectives are to find the optimal cycle time, preservation technology cost, and positive inventory time. The paper also proves the convexity of total cost through graphs with respect to decision variables. A sensitivity analysis of decision variables with respect to different inventory parameters is carried out.

scholarly journals Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 269 ◽  
Author(s):  
Wei Zhang ◽  
Zhipeng Li ◽  
Xuyang Gao ◽  
Yanjun Li ◽  
Yibing Shi
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Arrival Time ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Information Criteria ◽  
Echo Signal ◽  
Time Frequency ◽  
Ultrasonic Echo ◽  
Ultrasonic Anemometer

The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

Probabilistic Fatigue Assessment of a Notched Detail Taking Into Account Mean Stress Effects

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Abílio M. P. De Jesus ◽  
M. Luisa Ruiz Ripoll ◽  
Alfonso Fernández-Canteli ◽  
Enrique Castillo ◽  
Hélder F. S. G. Pereira
Keyword(s):  
Probabilistic Model ◽  
Fatigue Behavior ◽  
Material Behavior ◽  
Test Series ◽  
Model Parameters ◽  
Pressure Vessel Steel ◽  
Mean Stress ◽  
Vessel Steel ◽  
Life Data ◽  
R Ratio

Probabilistic fatigue models are required to account conveniently for the several sources of uncertainty arising in the prediction procedures, such as the scatter in material behavior. In this paper, a recently proposed stress-based probabilistic model is assessed using fatigue data available for the P355NL1 steel (a pressure vessel steel). The referred probabilistic model is a log-Gumbel regression model, able to predict the probabilistic Wöhler field (P–S–N field), taking into account the mean stress (or stress R-ratio) effects. The parameters of the probabilistic model are identified using stress-life data derived for the P355NL1 steel, from smooth specimens, for three distinct stress R-ratios, namely R = −1, R = −0.5, and R = 0. The model requires a minimum of two test series with distinct stress R-ratios. Since data from three test series is available, extrapolations are performed to test the adequacy of the model to make extrapolations for stress R-ratios other than those used in the model parameters assessment. Finally, the probabilistic model is used to model the fatigue behavior of a notched plate made of P355NL1 steel. In particular, the P–S–N field of the plate is modeled and compared with available experimental data. Cyclic elastoplastic analysis of the plate is performed since plasticity at the notch root is developed. The probabilistic model correlated appropriately the stress-life data available for the P355NL1 steel and was able to perform extrapolations for stress ratios other than those used in the model identification. The P–S–N field identified using data from smooth specimens led to consistent predictions of the P–S–N field for a notched plate, demonstrating the adequacy of the probabilistic model also to predict the probabilistic Wöhler field for notched components.

Towards Conditional Parameter Estimation for Automatic Model Structure Identification: Using Mixed-Integer Calibration for Model Development

2020 ◽  
Author(s):  
Diana Spieler ◽  
Juliane Mai ◽  
Bryan Tolson ◽  
James Craig ◽  
Niels Schütze
Keyword(s):  
Parameter Estimation ◽  
Mixed Integer ◽  
Structure Identification ◽  
Model Parameters ◽  
Suitable Model ◽  
Model Structure ◽  
Hydrologic Modelling ◽  
Vast Number ◽  
Calibration Process ◽  
Decision Variables

<p>A recently introduced framework for Automatic Model Structure Identification (AMSI) allows to simultaneously optimize model structure choices (integer decision variables) and parameter values (continuous decision variables) in hydrologic modelling. By combining the mixed-integer optimization algorithm DDS and the flexible hydrologic modelling framework RAVEN, AMSI is able to test a vast number of model structure and parameter combinations in order to identify the most suitable model structure for representing the rainfall runoff behavior of a catchment. The model structure and all potentially active model parameters are calibrated simultaneously. This causes a certain degree of inefficiency during the calibration process, as variables might be perturbed that are not currently relevant for the tested model structure. In order to avoid this, we propose an adaption of the current DDS algorithm allowing for conditional parameter estimation. Parameters will only be perturbed during the calibration process if they are relevant for the model structure that is currently tested. The conditional parameter estimation setup will be compared to the standard DDS algorithm for multiple AMSI test cases. We will show if and how conditional parameter estimation increases the efficiency of AMSI.</p>

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