Reduce Vehicle Travel Research Articles

An Integration of Real-Time Vehicle Routing and Mobile Technology in Poultry Distribution

Handling the distribution of broilers has special attention. The distribution of poultry should be done in the shortest possible time to decrease broiler mortality rate. Real-time monitoring of poultry distribution is expected to control the stress level of poultry due to the length of travel time by limiting the delivery time window. In order to control the distribution time and distance, it is necessary to integrate vehicle routing and mobile technology. By adopting the digital twin-enabled framework model and combining it with Google APIs, web and mobile applications are generated to accommodate the real-time traveling time, vehicle capacity, and pickup-delivery activity. There is a significant change in travel distance in one delivery cycle after implementing the developed technology. The total travel distance was 493 KM decreased to 436 KM, which means a decrease of 11.5% from the initial total travel distance. In addition, there was a change in the total penalty time (delay) from 102 minutes to 85 minutes, decreasing delay time by 16.6%. This mobile technology's development can indirectly increase vehicle utilization by not overloading the vehicle capacity and reducing vehicle travel distance.

A spatiotemporal control method at isolated intersections under mixed‐autonomy traffic conditions

AbstractWith the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human‐driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed‐autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal‐vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re‐optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process.

Electric Vehicle Charging and Car Dependency

We explore whether policies to promote electric vehicles (EVs) impede efforts to reduce vehicle travel. We hypothesize that the presence of EV chargers reduces respondents’ willingness to support (i) the removal of on-street parking to make space for bicycle lanes, and (ii) infill development on surface parking lots. We also hypothesize that the availability of EVs reduces public support for broader vehicle travel reduction policies. Using a randomized survey-based experiment, we find no evidence to support any of these hypotheses.

Evaluating Walkability in Budapest through Space Syntax Analysis

Walking is a fundamental and intuitive means of transportation both on an individual and urban scale, offering social and personal benefits like improved health, equity, and reduced greenhouse gas emissions. Despite these benefits, urban sprawl and increased car dependency remain significant challenges. To combat this trend, urban planners have emphasized walking as a way to reduce vehicle travel and emissions, while public health researchers recognize its potential to improve general health.This study explores the concept of walkability and its relationship to urban design, with a specific focus on two recently renovated areas in Budapest, namely Egyetem Square and Corvin Promenade. Utilizing space syntax analysis, a promising approach to developing a walkability index, this research investigates the functional aspects of these urban spaces and their impact on walkability.The study aims to demonstrate the potential of space syntax analysis in enhancing urban planning efforts aimed at promoting walkability. By analyzing the spatial configuration, visibility, and accessibility of the case studies, this research examines how the design and layout of these areas influence pedestrian movement patterns and the overall walkability experience.By demonstrating the potential of space syntax analysis in informing urban planning decisions, this study provides insights into the relationship between spatial configuration and walkability. The findings highlight the importance of well-connected street networks, clear pathways, and visually appealing urban elements in creating walkable environments. The results of the analysis contribute to the understanding of how urban design factors can be leveraged to enhance walkability and create sustainable, livable cities.

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks.

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

Incremental path planning: Reservation system in V2X environment

Previous work assumes that traffic flow evolves over time, and paths are planned based on traffic estimation and prediction, although such an assumption is simple and efficient for single vehicles, there is a self-contradictory problem when planning paths for multiple vehicles. If multiple vehicles choose the same uncongested road based on traffic forecasts, this may lead to congestion on that road, which in turn affects the efficiency of path planning. The V2X environment offers the possibility to solve the above problems. In this paper, a new perspective is developed where the traffic flow is considered as a superposition of spatiotemporal paths. From this perspective, a novel method is proposed in which the paths are planned incrementally according to the remaining spatiotemporal resources and the travel demands, which is referred to as Incremental Path Planning (IPP). IPP plans the paths of vehicles according to a predefined priority, after a vehicle’s path is planned, the occupancy of spatiotemporal resources is updated, and the remaining resources are then passed to the next vehicle for path planning. In IPP, an incrementally updated traffic model is proposed to obtain the traffic state. Based on this model, a time-dependent path search algorithm is proposed to reduce vehicle travel times. Simulation experiments based on real data sets have demonstrated the excellent performance of IPP in both improving traffic efficiency and driving experience.

Perceptions of surveillance: Exploring feelings held by Black community leaders in Boston toward camera enforcement of roadway infractions

Roadway camera enforcement programs have been found to effectively reduce vehicle travel speeds, as well as decrease the number and severity of collisions. Despite a wealth of evaluative research confirming this enforcement approach's aptitude at promoting safer roadway behavior, fewer than 50 % of US states currently host camera-based programs. Public opposition is frequently cited as the cause for the slow proliferation of this enforcement strategy. However, with public demand for police reform having an increasing presence on the national political stage, how might feelings toward camera technology currently stand among groups most marginalized by existing enforcement systems, and how might those feelings vary by type of enforcement application? Through a series of focus groups, this work centers Black voices on matters of surveillance and roadway enforcement by discussing sentiment toward camera programs with Black community leaders. This discussion is contextually situated in Boston, Massachusetts, where legislation that would allow for camera enforcement of roadway infractions is actively being deliberated in the State Senate. Findings culminate in a list of right-sizing and procedural recommendations for policy makers hoping to gain support for camera enforcement, improve roadway safety, and advance racial equity in our systems of policing and governance.

Can shared micromobility replace auto travel? Evidence from the U.S. urbanized areas between 2012 and 2019

Previous literature has shown mixed findings on whether bike or e-scooter share programs, collectively called shared micromobility, play a role in reducing vehicle travel. In this study, we focused on two types of shared micromobility (i.e., bikes—both regular and e-bikes—and e-scooters). We examined their impact on vehicle travel in urbanized areas in the United States between 2012 and 2019. Employing the difference-in-differences (DID) approach, we quantified the collective effect of shared micromobility on daily vehicle miles of travel (VMT) per capita at the urbanized area level. Our findings suggest that bike shares could reduce VMT while e-scooter share alone might not have a significant impact on lowering vehicle travel. Our results also indicate that there may be a synergistic effect of bike and scooter shares in VMT reduction when both types of shared micromobility are available in an urbanized area. Our findings also demonstrate regional variations in the impact of shared micromobility on vehicle travel.

Estimation of Vehicle Energy Consumption at Intersections Using Microscopic Traffic Models

This paper addresses issues related to modeling energy consumption and emissions using microscopic traffic simulations. This paper develops a method in which a traffic model is used to calculate the energy needed to travel through selected types of intersections. This paper focuses on energy consumption and derived values of calculated energy, which can be, for example, carbon dioxide emissions. The authors present a review of the scientific literature on the study of factors affecting energy consumption and emissions and methods to estimate them in traffic. The authors implemented an energy consumption model into a microsimulation software module to estimate results as a function of varying traffic volumes at selected types of intersections and for selected traffic organization scenarios. The results of the study show the lowest energy consumption and the lowest emissions when road solutions are selected that contribute to reducing vehicle travel times on the urban street network at higher average vehicle speeds. In addition, the positive impact of the share of electric vehicles in the traffic flow on the reduction of energy consumption and emissivity was estimated.

A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

The development of the smart city concept and inhabitants’ need to reduce travel time, in addition to society’s awareness of the importance of reducing fuel consumption and respecting the environment, have led to a new approach to the classic travelling salesman problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?”. At present, with the development of Internet of Things (IoT) devices and increased capabilities of sensors, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the aim is to provide a solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm Teacher Learner Based Optimization (TLBO). In addition, to improve performance, the solution is implemented by means of a parallel graphics processing unit (GPU) architecture, specifically a Compute Unified Device Architecture (CUDA) implementation.

Impacts of two-scale urban form and their combined effects on commute modes in U.S. metropolitan areas

This study explores the relationship between urban form at the metropolitan and neighborhood scales and the percentages of four individual modes to commute. It then looks at the combined effects of urban forms at the metropolitan scale in metropolitan areas in the continental United States using multi-level regression models with interactive terms between urban forms. This study contains the percentages for four commute modes: percentage of the vehicle (VT), public transit (PT), biking (BI), and walking (WK). As urban form factors, this study contains urban fragments, population density, job-resident balance (JRB), the percentage of green areas at the metropolitan scale, and distance to the nearest job center at the neighborhood scale. The results show that at the neighborhood scale, a farther distance from individual neighborhoods to the nearest job center is associated with higher VT, and lower PT, BI, and WK. At the metropolitan scale, urban form factors have a more significant association with the percentage of individual modes to commute in large MSAs compared with small MSAs in terms of population. Most urban form factors at the metropolitan scale are not independently associated with the use of commute modes, but their interactions are associated with the percentage of individual modes to commute. Particularly, the level of urban fragments and the percentage of green areas may transform the impact of high population density and JRB on the percentage of the individual modes to commute except for WK. This study provides planners and policymakers with important guidelines to establish combined urban form plans rather than single urban form oriented plans to reduce vehicle travel.

Crafting strong, integrated policy mixes for deep CO2 mitigation in road transport

Transport CO2 emissions continue to grow globally despite advances in low-carbon technology and goal setting by numerous governments. In this Perspective, we summarize available evidence for the effectiveness of climate policies and policy mixes for road transport relative to 2030 and 2050 mitigation goals implied by the Paris Agreement. Current policy mixes in most countries are not nearly stringent enough. We argue that most regions need a stronger, more integrated policy mix led by stringent regulations and complemented by pricing mechanisms as well as other efforts to reduce vehicle travel.

Green paradox: Factors affecting travel distances and fuel usages, evidence from Japanese survey

Reducing vehicle travel distances may eventually be a promising option for reducing oil dependence. However, the factors affecting travel distances have not yet been investi-gated, which makes the development of effective transportation-related policy difficult. Therefore, we explore factors influencing travel distances in Japan, namely, income group, region, interest in purchasing a hybrid car, travel purposes, vehicle type, and demographics, using data collected through surveys. Subsequently, we examine whether the factors increasing travel distances also increase fuel usage in the transportation sec-tor. Several findings emerge: First, hybrid ownership is positively correlated to travel distances regardless of income level and interest in purchasing a hybrid car (hybrid interest). Second, income level has a positive relationship with travel distances. How-ever, individuals in the high-income group show negative coefficients towards travel distances. The results also show that factors increasing travel distance also positively affect fuel usage except for hybrid ownership. For example, hybrid ownership is posi-tively correlated with both travel distances and fuel consumption, which is a form of the “green paradox.” Thus, the contribution of this study is that it provides evidence for the existence of direct rebound effects and wealth inequality in travel behavior and indicates the need for implementing policies based on individuals’ socio-demographical identity and type of vehicle ownership.

Intersection traffic signal optimisation considering the impact of upstream curbside bus stops

Transit-oriented travel mode is considered as one of the most effective travel strategies for improving the level of travel service and reducing travel time and delay. Curbside bus stops are often built to support the operation of the transit system, which leads to new bottlenecks of road segments near such stops and exerts a negative impact on traffic flows of neighbouring intersections. The authors propose a traffic signal optimisation model with the objective to minimise total travel delay by considering the interaction of traffic flows between the intersections and upstream curbside bus stops. First, transits’ following and lane-changing behaviours are explored by analysing their trajectories collected by unmanned vehicle aerial video detectors. Second, transits’ occupancy time at curbside bus stops is statistically analysed by using automatic vehicle location data. They then use an equivalent-flow analysis method to quantify the impact caused by transits’ actions of lane-changing and moving into/out stop basins (including the waiting process). Such impact is integrated into the algorithm of intersection traffic signal optimisation. They finally validate the proposed signal optimisation model based on real-time traffic data collected from Nanjing, China. It is revealed that the improved model can reduce vehicle travel delay by 4.3% at the tested intersection.

Parking behaviour: The curious lack of cruising for parking in San Francisco

Cruising for parking has long been perceived as a major source of congestion and emissions in urban areas, but recent empirical work suggests that parking may not be as onerous as folklore suggests, and that the amount of vehicle travel attributable to cruising is minimal. In this paper, we reconcile these perspectives through a dynamic programming model of parking search, and empirical insights from a large-scale GPS dataset in San Francisco and the California Household Travel Survey. We first draw a conceptual distinction between parking search, the time between the driver’s decision to park and when a parking space is taken; and cruising, defined as excess vehicle travel due to parking search. In places with little or no through traffic, up to half of traffic can be searching for parking, but cruising can be zero. We then operationalize this distinction through a dynamic programming model. The model predicts that when parking is perceived to be scarce, drivers are more willing to take a convenient available space, even if it is some distance from their destination. Counter-intuitively, scarce parking can even suppress vehicle travel as perceived parking scarcity leads drivers to stop short of their destinations and accept a longer walk. Empirical data from California indicate that neighborhood density (a proxy for parking availability) has little impact on cruising for parking, but increases walk distances from parking locations to final destinations. We conclude that cruising for parking is self-regulating, and that in certain circumstances parking scarcity can even reduce vehicle travel.

Examining interaction effects among land-use policies to reduce household vehicle travel: An exploratory analysis

Numerous studies have suggested that land-use policies can reduce vehicle travel through mode shifting and reduced trip lengths and generation of fewer or more efficient trips. The findings from previous studies also suggest that the combined effect of two or more land-use policies can be significant, although the effects of individual policies appear to be modest. These studies present area-wide impacts of land-use policies on travel and suggest that their effects are additive. However, very little is known about how each land-use policy interacts with the others at different levels of development intensity to reduce vehicle travel. In this study, we explore how three well-known land-use strategies (densification, mixed-use development, and street network improvement) interact with each other by testing possible combinations of land-use factors and focus on how these interactive effects vary by the level of development intensity. Employing ordinary least squares regression analysis using a dataset created for the Austin metropolitan statistical area (MSA) (using 2006 Austin Travel Survey data), we examine the impact of land use on household vehicle travel. Our findings suggest that interaction effects occur, but they vary by development intensity. The results of this study show the importance of considering both threshold (development intensity) and interaction (combination of policies) effects in understanding how land-use factors do and do not affect travel (based on their interactive opposed to only their direct and additive effects). Though this paper uses data from just one MSA and thus is merely suggestive, it does point to a possibly more nuanced use of the commonly prescribed planning and design policy variable to account for variation in effectiveness based on differences in development intensity. For example, we find that greater land-use intensification has higher efficacy in changing vehicle travel behavior in areas with relatively higher development intensity. Future research should include data from a broader array of metropolitan areas and incorporate additional predictor variables that were unavailable for this analysis.

Evaluation of Shared Space to Reduce Traffic Congestion

Pedestrian and vehicle interactions often lead to conflicts that bring about safety, traffic congestion, and priority or right of way issues. Common methods used in the past to combat said issues have largely relied on the principle of separating the motions of pedestrians and vehicles by means of bridges, tunnels, signals, and access restrictions. A different approach known as shared space aims to solve the same problems with a less structured and defined environment which instead places more reliance on human interaction and perception. Although it has been used in multiple scenarios across Europe with success, instances of shared spaces in the United States are few. In the past, the success of shared space has mainly focused on safety, aesthetic, and pedestrian use metrics, with little quantitative knowledge regarding the traffic congestion relief benefits. This research focuses on evaluating and quantifying the traffic congestion relief abilities of shared space designs utilizing Vissim traffic microsimulation software and the economic impact these changes can make. A major pedestrian crossing location on West Virginia University’s Downtown campus along a major urban arterial was chosen as the case location upon which the model was to be built. This location posed unique aspects, which made it a prime choice for this research as the major concern for years has been traffic congestion, in addition to pedestrian safety and aesthetic appeal. The results of the analysis show that shared space can reduce vehicle travel time by up to 50% and delays by 66%.

Optimization for routing vehicles of seafood product transportation

Recently, increasing usage of marine products is creating new challenges for businesses of marine products in terms of transportation that used to carry the marine products like seafood to the main warehouse. This can be a problem if the carrier fleet is limited, and there are time constraints in terms of the freshness of the marine product. There are many ways to solve this problem, including the optimization of routing vehicles. In this study, this strategy is to implement in the marine product business in Indonesia with such an expected arrangement of the company to optimize routing problem in transportation with time and capacity windows. Until now, the company has not used the scientific method to manage the routing of their vehicle from warehouse to the location of marine products source. This study will solve a stochastic Vehicle Routing Problems (VRP) with time and capacity windows by using the comparison of six methods and looking the best results for the optimization, in this situation the company could choose the best method, in accordance with the existing condition. In this research, we compared the optimization with another method such as branch and bound, dynamic programming and Ant Colony Optimization (ACO). Finally, we get the best result after running ACO algorithm with existing travel time data. With ACO algorithm was able to reduce vehicle travel time by 3189.65 minutes, which is about 23% less than existing and based on consideration of the constraints of time within 2 days (including rest time for the driver) using 28 tons capacity of truck and the companies need two units of vehicles for transportation.

Reducing Vehicle Travel for the Next Generation: Lessons from the 2001 and 2009 National Household Travel Surveys.

In the United States, per capita vehicle miles traveled (VMT) displayed a general declining trend in the new millennium. This paper focuses on identifying the factors that drove the recent VMT trend. Using sample data from the 2001 and 2009 National Household Travel Surveys (NHTS), this study investigated differences in travel patterns by age groups over time and explored factors associated with the recent decrease in per-capita VMT. The study results show that the daily auto mileage and number of auto trips significantly decreased in 2009, although the change was uneven across different age groups. Increased travel by public transit and walking and biking, along with urbanization efforts, partly explained the recent decrease in automobile use. The findings also suggest that population-specific strategies such as improving public transit services for younger people and upgrading walking facilities for the elderly may help reduce automobile travel demand. In addition, applications of information and communication technologies (ICTs) can offer promising alternatives to automobile travel.

Local Variations in the Impacts of Built Environments on Traffic Safety

This study examined the influence of built environments on crashes with different levels of injury severity, and employed Geographically Weighted Negative Binomial Regressions to test whether these relationships varied across different neighborhoods in Austin, Texas. The results showed that high-speed roads produced more total and fatal crashes, and their influence was stronger in downtown areas than in peripheral regions. Commercial and office areas experienced more injury crashes, especially in downtown locations. It is crucial to implement programs to reduce vehicle travel demand, retrofit high-speed roads, and design land areas that generate fewer vehicle trips, especially for downtown spaces.