scholarly journals A Policy-Sensitive Model of Parking Choice for Commercial Vehicles in Urban Areas

2020 ◽  
Vol 54 (3) ◽  
pp. 606-630 ◽  
Author(s):  
Giacomo Dalla Chiara ◽  
Lynette Cheah ◽  
Carlos Lima Azevedo ◽  
Moshe E. Ben-Akiva
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Commercial Vehicle ◽  
Choice Model ◽  
Discrete Event ◽  
Real Data ◽  
Behavior Modeling ◽  
Model Parameters ◽  
Commercial Vehicles ◽  
Real World Data

Understanding factors that drive the parking choice of commercial vehicles at delivery stops in cities can enhance logistics operations and the management of freight parking infrastructure, mitigate illegal parking, and ultimately reduce traffic congestion. In this paper, we focus on this decision-making process at large urban freight traffic generators, such as retail malls and transit terminals, that attract a large share of urban commercial vehicle traffic. Existing literature on parking behavior modeling has focused on passenger vehicles. This paper presents a discrete choice model for commercial vehicle parking choice in urban areas. The model parameters were estimated by using detailed, real-world data on commercial vehicle parking choices collected in two commercial urban areas in Singapore. The model analyzes the effect of several variables on the parking behavior of commercial vehicle drivers, including the presence of congestion and queueing, attitudes toward illegal parking, and pricing (parking fees). The model was validated against real data and applied within a discrete-event simulation to test the economic and environmental impacts of several parking measures, including pricing strategies and parking enforcement.

Developing a Measure of Traffic Congestion: Fuzzy Inference Approach

2002 ◽  
Vol 1802 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Khaled Hamad ◽  
Shinya Kikuchi
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Supply And Demand ◽  
Travel Speed ◽  
Flow Speed ◽  
Inference Process ◽  
Real World Data ◽  
Total Travel Time ◽  
The Individual

Many measures have been proposed to represent the status of traffic conditions on arterial roadways in urban areas. The debate about what is the most appropriate measure continues. In a contribution to the debate, another approach was offered. Traditionally, two general approaches exist. One is based on the relationship between supply and demand. The other is a measure relative to the most acceptable status of service quality. The latter measure allows the public to relate to their travel experience. In either case, however, derivation of measures of congestion involves uncertainty because of imprecision of the measurement, the traveler’s perception of acceptability, variation in sample data, and the analyst’s uncertainty about causal relations. A measure is proposed that is a composite of two traditional measures, travel speed and delay. In recognition of the uncertainty, a fuzzy inference process was proposed. The inputs are travel speed, free-flow speed, and the proportion of very low speed in the total travel time. These values were processed through fuzzyrule-based inference. The outcome was a single congestion index value between 0 and 1, where 0 is the best condition and 1 is the worst condition. The process was demonstrated using real-world data. The results were compared with those of the Highway Capacity Manual. Although no conclusion can be drawn about the best measure of congestion, the proposed inference process allows the mechanism to combine different measures and also to incorporate the uncertainty in the individual measures so that the composite picture of congestion can be reproduced.

scholarly journals Assessing Overnight Parking Infrastructure Policies for Commercial Vehicles in Cities Using Agent-Based Simulation

2020 ◽  
Vol 12 (7) ◽  
pp. 2673 ◽  
Author(s):  
Raja Gopalakrishnan ◽  
André Romano Alho ◽  
Takanori Sakai ◽  
Yusuke Hara ◽  
Lynette Cheah ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Choice Model ◽  
Critical Element ◽  
Heavy Vehicles ◽  
Negative Externalities ◽  
Commercial Vehicles ◽  
Agent Based ◽  
Urban Freight ◽  
Urban Freight Transport

Urban freight transport is primarily fulfilled by commercial road vehicles. Within cities, overnight parking is a critical element influencing commercial vehicle operations, particularly for heavy vehicles with limited parking options. Providing adequate overnight parking spaces for commercial vehicles tends to be a challenge for urban planners. Inadequate parking supply can result in illegal parking and additional vehicle kilometers traveled, contributing to traffic congestion and air pollution. The lack of tools for evaluating the impacts of changing parking supply is an impediment in developing parking-related solutions that aim to minimize the negative externalities. In this study, we develop an overnight parking choice model for heavy commercial vehicles and integrate it with SimMobility, an agent-based urban simulation platform, demonstrating the potential of this tool for policy evaluation. Using simulations applied to a case study in Singapore, we compare two parking supply scenarios in terms of vehicle kilometers traveled due to changes in the first and last trips of vehicle tours, as well as resulting impacts in traffic flows.

Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

2018 ◽  
Vol 8 (5) ◽  
pp. 84
Author(s):  
Vineet Kumar Gupta ◽  
Sriram Yadav
Keyword(s):  
Public Transportation ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Travel Demand ◽  
Choice Model ◽  
Nearest Neighbor Search ◽  
Hill Climbing ◽  
K Nearest Neighbor ◽  
Real World Data ◽  
Bus Routing

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Based on mobility patterns, propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. It also leverages the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips. GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Ola, Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. Now, study a new kind of query – Modified k-Nearest Neighbor Search with Hill Climbing (MkNNHC), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, an MkNNHC query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RkNNT query. Then introduce a filter refinement framework for processing MkNNHC queries using the proposed indexes. Experiments on real datasets demonstrate the efficiency and scalability of our approaches.

scholarly journals A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 722 ◽  
Author(s):  
Jorge Zambrano-Martinez ◽  
Carlos Calafate ◽  
David Soler ◽  
Lenin-Guillermo Lemus-Zúñiga ◽  
Juan-Carlos Cano ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Traffic Management ◽  
Real Data ◽  
Prediction Equation ◽  
City Management ◽  
Traffic Demand ◽  
Traffic Conditions

Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.

Maximum Likelihood Estimation for Combined Travel Choice Model Parameters

1998 ◽  
Vol 1645 (1) ◽  
pp. 160-169 ◽  
Author(s):  
James E. Hicks ◽  
Mounir M. Abdel-Aal
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Minimization Problem ◽  
Urban Areas ◽  
Choice Model ◽  
Stable Solution ◽  
Bilevel Optimization ◽  
Estimation Problem ◽  
Parameter Estimates ◽  
Model Parameters

Equilibrium models of combined location and travel choices solve for the modal link flow pattern, which simultaneously solves a constrained minimization problem and satisfies a set of equilibrium conditions characterizing a rational behavior for traveler choices in an urban transportation system. The minimization problem typically is made to be representative of the particular urban area being studied by including coefficients of travel costs and travel choices that have been estimated from locally available observed data. For large urban areas, in practice, it is possible to derive interzonal travel times and costs only from the travel model, because suitable observed data are nonexistent. In this case, the estimation problem is a function of the travel model variables and, at the same time, the travel model is a function of the parameters determined by the estimation problem. Procedures to computationally search for a stable solution to this bilevel optimization problem have been addressed with limited success. The parameter estimation is solved in an iterative procedure in which first parameters are held fixed and the travel model is solved, then travel patterns are held fixed and the maximum likelihood parameters are solved by the Newton-Raphson method. Each successive parameter estimation resulting from these two steps results in a new set of parameter values for the next iteration until stable values for the parameters are achieved. The quality of the convergence of the parameter estimates is reported.

The Use of Queueing and Simulative Analyses to Improve an Overwhelmed Pharmacy Call Center

2010 ◽  
Vol 23 (5) ◽  
pp. 492-495 ◽  
Author(s):  
T. Eugene Day ◽  
W. Max Li ◽  
Armann Ingolfsson ◽  
Nathan Ravi
Keyword(s):  
Traffic Congestion ◽  
Call Center ◽  
Medical Center ◽  
Discrete Event ◽  
Simulation Models ◽  
Real World Data ◽  
Help Desk ◽  
Staffing Levels ◽  
Event Dynamic ◽  
Telephone Traffic

Like many others, the St. Louis Veterans Administration Medical Center (VAMC) Pharmacy help desk receives far more calls than can be processed by current staffing levels. The objective of the study is to improve pharmaceutical services provided by the call center, by using queueing theory and discrete event dynamic simulation to analyze incoming telephone traffic to the help desk. Queueing and simulation models using both archival and hand-gathered data over a 1-year period were created, compared, and presented in order to determine the minimum quantities of staff needed to reach the desired service threshold. The simulation model was validated in comparison with real-world data. Results suggest that telephone traffic congestion in this setting may be alleviated by increasing the number of staff responsible for telephone services from 2 to 6 throughout the week, with an additional one serving on Monday. Both queueing and simulative models can be used to improve overwhelm pharmacy call centers, by determining the theoretical minimal staff needed to reach a service threshold.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

2019 ◽  
Vol XVI (2) ◽  
pp. 1-11
Author(s):  
Farrukh Jamal ◽  
Hesham Mohammed Reyad ◽  
Soha Othman Ahmed ◽  
Muhammad Akbar Ali Shah ◽  
Emrah Altun
Keyword(s):  
Real Data ◽  
Continuous Model ◽  
Model Parameters ◽  
Data Set ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
Proposed Model ◽  
Probability Weighted Moment ◽  
Record Statistics ◽  
Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

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