Analysis of the Effects of Connected–Automated Vehicle Technologies on Travel Demand

2017 ◽  
Vol 2625 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Joshua Auld ◽  
Vadim Sokolov ◽  
Thomas S. Stephens
Keyword(s):  
Traffic Flow ◽  
Travel Behavior ◽  
Energy Use ◽  
Travel Demand ◽  
Transportation System ◽  
Transportation Systems ◽  
Potential Range ◽  
Cruise Control ◽  
Automated Vehicle ◽  
Positive Effects

Connected–automated vehicle (CAV) technologies are likely to have significant effects not only on how vehicles operate in the transportation system, but also on how individuals behave and use their vehicles. While many CAV technologies—such as connected adaptive cruise control and ecosignals—have the potential to increase network throughput and efficiency, many of these same technologies have a secondary effect of reducing driver burden, which can drive changes in travel behavior. Such changes in travel behavior—in effect, lowering the cost of driving—have the potential to increase greatly the utilization of the transportation system with concurrent negative externalities, such as congestion, energy use, and emissions, working against the positive effects on the transportation system resulting from increased capacity. To date, few studies have analyzed the potential effects on CAV technologies from a systems perspective; studies often focus on gains and losses to an individual vehicle, at a single intersection, or along a corridor. However, travel demand and traffic flow constitute a complex, adaptive, nonlinear system. Therefore, in this study, an advanced transportation systems simulation model—POLARIS—was used. POLARIS includes cosimulation of travel behavior and traffic flow to study the potential effects of several CAV technologies at the regional level. Various technology penetration levels and changes in travel time sensitivity have been analyzed to determine a potential range of effects on vehicle miles traveled from various CAV technologies.

Assessment of Influence of Land Use–Transportation System on Travel Behavior

1997 ◽  
Vol 1607 (1) ◽  
pp. 105-115 ◽  
Author(s):  
Michael G. McNally ◽  
Anup Kulkarni
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Transportation System ◽  
Transportation Systems ◽  
Neighborhood Design ◽  
Preliminary Assessment ◽  
Modeling Framework ◽  
Systematic Methodology ◽  
Definition Of ◽  
Planned Unit Development

An empirical assessment of the interaction between the land use–transportation system and travel behavior is presented. A methodology for identifying a range of land use–transportation systems by a clustering technique with network and land use inputs was developed. Twenty neighborhoods from Orange County, California, were considered in this process. Three groups, or themes, were found to best represent the neighborhoods in the sample area: one each associated with the conventional definition of traditional and neotraditional neighborhood design (TND) and planned unit development (PUD) neighborhoods and one representing neighborhoods that blend characteristics of TND and PUD. Conventional measures of individual travel behavior were compared with an analysis of variance between the themes to identify significant differences, controlling for socioeconomic characteristics. Research results include the development of (a) a systematic methodology to identify a more explicit land use–transportation dimension, (b) an estimate of the potential effectiveness of design-oriented solutions to reduce automobile congestion by using the developed themes, and (c) a preliminary assessment of the extent to which development themes can be used to improve the current modeling framework.

scholarly journals Calibration of MATSim in the context of natural hazards in Belgium

2016 ◽  
Author(s):  
Mario Cools ◽  
Ismaïl Saadi ◽  
Ahmed Mustafa ◽  
Jacques Teller
Keyword(s):  
Travel Behavior ◽  
Large Scale ◽  
Travel Demand ◽  
Scale Effects ◽  
Transportation Network ◽  
Transportation Systems ◽  
Generation Process ◽  
Traffic Pattern ◽  
Synthetic Population ◽  
River Floods

In Belgium, river floods are among the most frequent natural disasters and they may cause important changes on travel demand. In this regard, we propose to set up a large scale scenario using MATSim for guarantying an accurate assessment of the river floods impact on the transportation systems. In terms of inputs, agent-based models require a base year population. In this context, a synthetic population with a respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into the dynamic traffic assignment simulator, i.e. MATSim. With respect to data, households travel surveys are the main inputs for synthesizing the populations. Besides, a steady-state inundation map is integrated within MATSim for simulating river floods. To our knowledge, very few studies have focused on how river floods affect transportation systems. In this regard, this research will undoubtedly provide new insights in term of methodology and traffic pattern analysis under disruptions, especially with regard to spatial scale effects. The results indicate that at the municipality level, it is possible to capture the effects of disruptions on travel behavior. In this context, further disaggregation is needed in future studies for identifying to what extent results are sensitive to disaggregation. In addition, results also suggest that the target sub-population exposed to flood risk should be isolated from the rest of the travel demand to reach have more sensitive effects.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4098

scholarly journals Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound

2019 ◽  
Author(s):  
Morteza Taiebat ◽  
Samuel Stolper ◽  
Ming Xu
Keyword(s):  
Travel Behavior ◽  
Energy Use ◽  
Travel Demand ◽  
Rebound Effect ◽  
Time Cost ◽  
Fuel Cost ◽  
Automated Vehicles ◽  
Time Costs ◽  
Induced Travel ◽  
The Impact

Connected and automated vehicles (CAVs) are expected to yield significant improvements in safety, energy efficiency, and time utilization. However, their net effect on energy and environmental outcomes is unclear. Higher fuel economy reduces the energy required per mile of travel, but it also reduces the fuel cost of travel, incentivizing more travel and causing an energy “rebound effect.” Moreover, CAVs are predicted to vastly reduce the time cost of travel, inducing further increases in travel and energy use. In this paper, we forecast the induced travel and rebound from CAVs using data on existing travel behavior. We develop a microeconomic model of vehicle miles traveled (VMT) choice under income and time constraints; then we use it to estimate elasticities of VMT demand with respect to fuel and time costs, with fuel cost data from the 2017 United States National Household Travel Survey (NHTS) and wage-derived predictions of travel time cost. Our central estimate of the combined price elasticity of VMT demand is -0.4, which differs substantially from previous estimates. We also find evidence that wealthier households have more elastic demand, and that households at all income levels are more sensitive to time costs than to fuel costs. We use our estimated elasticities to simulate VMT and energy use impacts of full, private CAV adoption under a range of possible changes to the fuel and time costs of travel. We forecast a 2-47% increase in travel demand for an average household. Our results indicate that backfire – i.e., a net rise in energy use – is a possibility, especially in higher income groups. This presents a stiff challenge to policy goals for reductions in not only energy use but also traffic congestion and local and global air pollution, as CAV use increases.

Implementation of Surveying Techniques in the Route Selection for Baghdad Metro Tube

2021 ◽  
Author(s):  
Saad I Sarsam ◽  
Keyword(s):  
Land Use ◽  
Public Transportation ◽  
Travel Demand ◽  
Transportation Network ◽  
Transportation System ◽  
Transportation Systems ◽  
Google Earth ◽  
Route Selection ◽  
Transit Services ◽  
Selection For

Transportation systems play a central role in a sustainable society by providing mobility for people, goods, and services. Significant sustainability benefits are being derived through the improvements in transportation network efficiency, use of alternative modes and multimodality, integration of sustainable design, better integration of land use and transportation systems. Sustainable transportation system usually refers to any means of transportation which has low impact on the environment, affordable to users and can balance the current and future needs. This work covers the implementation of surveying techniques in the route selection for Baghdad Metro Tube. The travel demand has been assessed through an extensive travel potential survey. The public bus terminals were considered as a major source of data. The number of passengers using the present public transportation system from each bus terminal and for each route to various destinations has been recorded. The passenger supply points have been indicated by latitude and longitude that define the bus stop and the proposed metro route using global positioning system GPS. A passenger counting data was collected concerning the present use of public transport. A line indicates travel from one area to another and a grid was constructed. The present bus routes were identified, and the 28 major and minor public transportation terminals, which represent the passenger trip origin and destination nodes, were detected using GPS. The bus terminals were also positioned by the GPS and affixed. The recent land use of Baghdad urban area and the existing transportation network as obtained from Google earth were utilized in the geographic information system GIS environment. Travel corridors are identified and analyzed according to their existing right-of-way conditions, transit services, land use, and demographics.The positive and negative attributes of each corridor with regards to their potential for supporting transitoriented development TOD and higher capacity transit services have been determined through optimization process in the GIS. Finally, five corridors of the highest trip potential have been selected and proposed.

scholarly journals The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Gongxing Yan ◽  
Yanping Chen
Keyword(s):  
Big Data ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
The Other ◽  
The City

The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.

Model-Based Long-Range Transportation Planning Tool for New Jersey

2002 ◽  
Vol 1817 (1) ◽  
pp. 93-101
Author(s):  
Anthony J. De John ◽  
Robert Miller ◽  
Kyle B. Winslow ◽  
Jennifer J. Grenier ◽  
Deborah A. Cano
Keyword(s):  
New Jersey ◽  
Long Range ◽  
Travel Demand ◽  
Management Strategies ◽  
Transportation System ◽  
Transportation Systems ◽  
Analysis Tool ◽  
Demand Model ◽  
Demand Models ◽  
The Impact

The New Jersey Department of Transportation (NJDOT) updates its long-range transportation plan every 5 years. The plan sets forth strategies, provides a framework for directing investment, and identifies financial resources needed to sustain the plan’s vision. Setting the direction of a long-range transportation program revolves around forecasting future transportation conditions and managing investments to address future needs. An analysis tool was needed to help assess the impact of growth on the statewide transportation system and predict system performance based on multimodal strategic investments. The development and use of an analysis tool based on a travel demand model to assess congestion and mobility issues in 2025 are described. The analysis tool linked the state’s three metropolitan planning organization (MPO) regional travel demand models to perform a statewide assessment. Although the models were run independently, methods were developed to provide a common basis for forecasting future travel conditions. The models used MPO-generated trend-based growth in population and employment through 2025. Multimodal transportation supply and demand strategies, including transit improvements, capacity improvements, transportation demand management strategies, and intelligent transportation systems-transportation system management strategies, were simulated and tested to assess what types and combinations of improvements would be needed to relieve congestion and improve mobility. The tool proved very helpful in defining transportation needs and providing input to a financial assessment. The testing indicated that no single strategy is likely to improve future travel conditions, but a combination of multimodal strategies offers significant improvements over congestion levels predicted for 2025 if no improvements are made.

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.

scholarly journals Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee
Keyword(s):  
User Interface ◽  
Situation Awareness ◽  
User Interfaces ◽  
Situational Awareness ◽  
Selection Criterion ◽  
Auditory Modality ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Positive Effects ◽  
High Urgency

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

scholarly journals Automated vehicle-involved traffic flow studies: A survey of assumptions, models, speculations, and perspectives

2021 ◽  
Vol 127 ◽  
pp. 103101
Author(s):  
Haiyang Yu ◽  
Rui Jiang ◽  
Zhengbing He ◽  
Zuduo Zheng ◽  
Li Li ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Automated Vehicle
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