Revisiting the notion of induced traffic through a matched-pairs study

In investigating the question of the existence of "induced demand" in connection with highway expansion projects, Hansen et al. (1993) studied eighteen California state highway segments whose capacities had been improved in the early 1970s. For the present study, these segments were paired with control segments that matched the improved segments to unimproved ones with regard to facility type, region, approximate size, and initial volumes and congestion levels. Taking annual data for average daily traffic (ADT) and design-hour-traffic-to-capacity (DTC) ratios during the 21 years 1976–1996, three approaches were used to compare growth rates between the improved and unimproved segments: overall growth comparisons for the matched pairs, repeated measures analysis, and analysis of matched mean profiles. We found the growth rates between the two types of segments to be statistically and practically indistinguishable, suggesting that the capacity expansions, in and of themselves, had a negligible effect on traffic growth over the period studied. Reasons for the differences between these results and those of aggregate cross-sectional models finding a significant induced demand effect are discussed. Our analyses suggest that the aggregate models may overestimate induced traffic due to the attribution of at least a fraction of the observed traffic growth to "induced demand" rather than to some of the confounding factors which were not controlled for in such studies. At the same time, it is noted that the traffic induced by capacity expansion may in certain circumstances be larger than that observed in the present study, with the effect of new highway construction on traffic growth being a prime candidate for scrutiny in this regard. The results of this study nonetheless suggest that, for existing facilities, the size of the induced-traffic effect that can be attributed to capacity enhancements may be sufficiently small that its detection in a case-control study would be difficult, if not impossible, without a substantially larger sample size.     

Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway

The main purpose of this study was to investigate the predictability of travel time with a model based on travel time data measured in the field on an interurban highway. Another purpose was to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-kilometre-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the majority of the forecasts were close to the actual measured values. Consequently, use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times.     

A general corridor model for designing plug-in electric vehicle charging infrastructure to support intercity travel

This paper proposes to optimally configure plug-in electric vehicle (PEV) charging infrastructure for supporting long-distance intercity travel using a general corridor model that aims to minimize a total system cost inclusive of infrastructure investment, battery cost and user cost. Compared to the previous work, the proposed model not only allows realistic patterns of origin–destination demands, but also considers flow-dependent charging delay induced by congestion at charging stations. With these extensions, the model is better suited to performing a sketchy design of charging infrastructure along highway corridors. The proposed model is formulated as a mixed integer program with nonlinear constraints and solved by a specialized metaheuristic algorithm based on Simulated Annealing. Our numerical experiments show that the metaheuristic produces satisfactory solutions in comparison with benchmark solutions obtained by a mainstream commercial solver, but is more computationally tractable for larger problems. Noteworthy findings from numerical results are: (1) ignoring queuing delay inducted by charging congestion could lead to suboptimal configuration of charging infrastructure, and its effect is expected to be more significant when the market share of PEVs rises; (2) in the absence of the battery cost, it is important to consider the trade-off between the costs of charging delay and the infrastructure; and (3) building long-range PEVs with the current generation of battery technology may not be cost effective from the societal point of view.     

The Downs–Thomson paradox with imperfect mode substitutes and alternative transit administration regimes

The Downs–Thomson paradox (D–T paradox) occurs when expansion of a congested and untolled highway undermines scale economies of a competing transit service, leaving users of both modes worse off. The standard analysis of the D–T paradox is based on several stringent assumptions: fixed total travel demand, perfect substitutability between automobile and transit trips, and no transit crowding. This paper re-examines the paradox when these assumptions are relaxed while retaining the usual assumption that there is no congestion interaction between the modes. It also broadens consideration to alternative transit administration regimes. In the standard treatment the transit operator is obliged to cover its costs. In this paper we also study two other regimes: transit profit maximization, and system-wide welfare maximization with no financing constraint. We examine how the transit system operator responds to highway capacity expansion in each regime, and how this affects welfare for drivers and transit users. We show that in all regimes the full price of transit declines only if the full price of driving falls as well. Thus, drivers are more likely to benefit from highway expansion than transit riders. The D–T paradox cannot occur in the profit maximization or unconstrained welfare maximization regimes. In the traditional self-financing regime transit service deteriorates, but the D–T paradox is not inevitable. Numerical analysis suggests that it can occur only when automobile and transit trips are nearly perfect substitutes.     

Asymmetric responses of highway travel demand to changes in fuel price: An explanation via fuel price uncertainty

Previous research has examined asymmetric effects of fuel price uncertainty on energy demand. If we consider that energy demand is related to travel demand, the changes in fuel prices may have asymmetric effects on highway travel demand via fuel price uncertainty. In other words, when in general fuel price is steadily rising, the highway traffic volume decreases by a small percentage. On the other hand, the highway traffic volume increases by a large percentage when fuel prices are falling. We hypothesize that the uncertainty in fuel prices generates this kind of asymmetric effect on highway traffic volume in Korea. We use the Korean monthly fuel price and highway traffic volume data from 2001 to 2009, and define the intra-month (weekly) fuel price changes as monthly fuel price volatility which is a proxy for monthly fuel price uncertainty. We found that the direction of the change in fuel prices had asymmetric effects on highway travel demand and that the fuel price uncertainty led drivers to respond asymmetrically to the changes in fuel prices.     

Online Prediction of Travel Time: Experience From a Pilot Trial

Abstract

This study was designed to present an online model which predicted travel times on an interurban two-lane two-way highway section on the basis of field measurements. The study included two parts: an evaluation of the performance of the model, and an examination of the possibility to improve the model in case of unsatisfactory performance. The model was based on MLP neural networks. The main results of the evaluation showed that the prediction model outperformed a non-predictive system. However, the model for one section had not performed as well during the trial period as was expected. This might be due to a slight change in the congestion phenomenon. After further development, the findings showed that the model could be improved considerably with new data. The main implication was that even a simple prediction model improves the quality of travel time information substantially, compared to estimates based directly on the latest measurements.     

The congestion multiplier: Variations in motorists’ valuations of travel time with traffic conditions

The value of travel time savings in part depends upon the disutility of the travel time that is saved and partly on the use to which the time saved is put. It has long been recognised that the disutility of the time spent travelling also depends upon a wide range of factors such as the journey length or the effort, comfort and safety associated with travelling.Hence we might expect the value of motorists’ travel time to vary with the traffic conditions as represented by the degree of congestion, in part to reflect the more difficult driving environment when there are more vehicles, but also a higher sense of frustration, similar to that associated with waiting time and contributing to its premium valuation.In this context, and despite the predominance of car travel in developed countries, the empirical evidence specifically relating to car values of travel time tends to fail to distinguish between different types of time according to the degree of congestion. Thus we are often left unclear as to precisely what type of time has been valued. Moreover, when a distinction is made, it tends to be into a simple dichotomy of congested and uncongested traffic.This paper provides new evidence on the variation in the valuation of motorists’ travel time savings across a finer gradation of types of time than has been hitherto attempted. This is obtained from the same Stated Choice exercise conducted in the United Kingdom and the United States. The paper also provides an extensive account of previous research into how congestion impacts on motorists’ values of time.     

The sequenced activity mobility simulator (SAMS): an integrated approach to modeling transportation, land use and air quality

The persistence of environmental problems in urban areas and the prospect of increasing congestion have precipitated a variety of new policies in the USA, with concomitant analytical and modeling requirements for transportation planning. This paper introduces the Sequenced Activity-Mobility Simulator (SAMS), a dynamic and integrated microsimulation forecasting system for transportation, land use and air quality, designed to overcome the deficiencies of conventional four-step travel demand forecasting systems. The proposed SAMS framework represents a departure from many of the conventional paradigms in travel demand forecasting. In particular, it aims at replicating the adaptative dynamics underlying transportation phenomena; explicitly incorporates the time-of-day dimension; represents human behavior based on the satisficing, as opposed to optimizing, principle; and endogenously forecasts socio-demographic, land use, vehicle fleet mix, and other variables that have traditionally been projected externally to be input into the forecasting process.     

Modeling Travel Time Under ATIS Using Mixed Linear Models

The objective of this paper is to model travel time when drivers are equipped with pre-trip and/or en-route real-time traffic information/advice. A travel simulator with a realistic network and real historical congestion levels was used as a data collection tool. The network included 40 links and 25 nodes. This paper presents models of the origin-to-destination travel time and en-route short-term route (link) travel time under five different types and levels of advanced traveler information systems (ATIS). Mixed linear models with the repeated observation's technique were used in both models. Different covariance structures (including the independent case) were developed and compared. The effect of correlation was found significant in both models. The trip travel time analysis showed that as the level of information increases (adding en-route to the pre-trip and advice to the advice-free information), the average travel time decreases. The model estimates show that providing pre-trip and en-route traffic information with advice could result in significant savings in the overall travel time. The en-route short-term (link) travel time analysis showed that the en-route short-term (link) information has a good chance of being used and followed. The short-term qualitative information is more likely to be used than quantitative information. Learning and being familiar with the system that provides the information decreases en-route short-term delay.     

Effects of the Los Angeles transit strike on highway congestion

In this paper we investigate the effect of the Los Angeles transit strike on highway congestion through analysis of highway sensor data, using both a before-and-after comparison, and a control group comparison. We found that average 5:00 a.m. to 10:00 p.m. traffic speeds declined by as much as 20% during the strike, and the average length of the rush period increased by as much as 200%, even though increases in traffic were small, despite the fact that transit riders constitute a small fraction of the traveling population. Speeds declined the most at locations upstream from the places where queues normally end. We believe that highways are especially susceptible to congestion during strikes because travelers have little opportunity to adjust and equilibrate their travel patterns, as is possible during ordinary periods of traffic growth.     

Fully autonomous vehicles: analyzing transportation network performance and operating scenarios in the Greater Toronto Area,Canada

Fully autonomous vehicles (AVs) have the potential to considerably change urban mobility in the future. This study simulates potential AV operating scenarios in the Greater Toronto Area (GTA), Canada, and assesses transportation system performance on a regional level. For each scenario, the base capacities of certain types of road links are modified to simulate the theoretical increase in throughput enabled by AV driving behavior. Another scenario examines driverless parking operations in downtown Toronto. Simulation results indicate that the increased attractiveness of freeways relative to other routes leads to slightly increased average travel distance as vehicles divert to access higher capacity road links. Average travel time is found to decrease by up to one-fifth at the 90% AV market penetration level. Concurrently, localized increases in congestion suggest that proactive transportation planning will be needed to mitigate negative consequences of AV adoption, especially in relation to induced demand for personal automobile travel.     

衡水市冬季公交免费乘坐政策实施效果评估

优先发展公共交通,倡导绿色出行方式,可以缓解交通拥堵,减少交通污染。河北省衡水市在国内率先实施了冬季公交免费乘坐政策,政策出台的初衷是为了改善衡水市空气质量,经综合评估后,该政策在提高公交乘客满意度、增强公共交通吸引力和缓解交通拥堵等方面取得了明显成效,为国内三、四线城市的公共交通优先发展提供了有益的经验借鉴。     

Analysis of Metropolitan Highway Capacity and the growth in vehicle miles of travel

A number of recent studies have examined the hypothesis of induced travel in an attempt to quantify the phenomenon (Hansen & Huang 1997; Noland, forthcoming). No study has yet attempted to adjust for potential simultaneity bias in the results. This study addresses this issue by the use of an instrumental variable (two stage least squares) approach. Metropolitan level data compiled by the Texas Transportation Institute for their annual congestion report is used in the analysis and urbanized land area is used as an instrument for lane miles of capacity. While this is not an ideal instrument, results still suggest a strong causal relationship but probably that most previous work has had an upward bias in the coefficient estimates. The effect of lane mile additions on VMT growth is forecast and found to account for about 15% of annual VMT growth with substantial variation between metropolitan areas. This effect appears to be closely correlated with percent growth in lane miles, suggesting that rapidly growing areas can attribute a greater share of their VMT growth to growth in lane miles.     

Urban highway congestion

This article examines urban highway congestion pricing in the instance in which it is not possible to levy a congestion toll on a major portion of the urban road system. This case is pertinent because of technical and/or political constraints. The article uses economic theory and numerical examples to show that the optimum second-best toll can vary appreciably from the optimal tolls in a regime in which efficient tolls can be imposed on all routes.     

Optimal locations and travel time display for variable message signs

This paper first develops a network equilibrium model with the travel time information displayed via variable message signs (VMS). Specifically, the equilibrium considers the impact of the displayed travel time information on travelers’ route choices under the recurrent congestion, with the endogenous utilization rates of displayed information by travelers. The existence of the equilibrium is proved and an iterative solution procedure is provided. Then, we conduct the sensitivity analyses of the network equilibrium and further propose a paradox, i.e., providing travel time information via VMS to travelers may degrade the network performance under some poor designs. Therefore, we investigate the problem of designing the VMS locations and travel time display within a given budget, and formulate it as a mixed integer nonlinear program, solved by an active-set algorithm. Lastly, numerical examples are presented to offer insights on the equilibrium results and optimal designs of VMS.     

Rationing and pricing strategies for congestion mitigation: Behavioral theory,econometric model,and application in Beijing

Some travel demand management policies such as road pricing have been widely studied in literature. Rationing policies, including vehicle ownership quota and vehicle usage restrictions, have been implemented in several megaregions to address congestion and other negative transportation externalities, but not well explored in literature. Other strategies such as Vehicle Mileage Fee have not been well accepted by policy makers, but attract growing research interest. As policy makers face an increasing number of policy tools, a theoretical framework is needed to analyze these policies and provide a direct comparison of their welfare implications such as efficiency and equity. However, such a comprehensive framework does not exist in literature. To bridge this gap, this study develops an analytical framework for analyzing and comparing travel demand management policies, which consists of a mathematical model of joint household vehicle ownership and usage decisions and welfare analysis methods based on compensating variation and consumer surplus. Under the assumptions of homogenous users and single time period, this study finds that vehicle usage rationing performs better when relatively small percentages of users (i.e. low rationing ratio) are rationed off the roads and when induced demand elasticity resulting from congestion mitigation is low. When the amount of induced demand exceeds a certain level, it is shown analytically that vehicle usage restrictions will always cause welfare losses. When the policy goal is to reduce vehicle travel by a fixed portion, road pricing provides a larger welfare gain. The performance of different policies is influenced by network congestion and congestibility. This paper further generalizes the model to consider heterogenous users and demonstrates how it can be applied for policy analysis on a real network after careful calibration.     

Tradeoffs between Time Allocations to Maintenance Activities/Travel and Discretionary Activities/Travel

This paper focuses on the tradeoff in time allocation between maintenance activities/travel and discretionary activities/travel. We recognize that people generally must travel a minimum amount of time in order to allocate one unit of time to the activity. This minimum amount of travel is represented by the travel time price, a ratio obtained by dividing the total amount of time traveling to maintenance or discretionary activities by the total amount of time spent on activities of the same type; it is the time equivalent of the monetary price for performing an activity. Using the San Francisco Bay Area 1996 Household Travel Survey data and applying the Almost Ideal Demand System (AIDS) of demand equations, we found that with respect to the time equivalent of income elasticities of maintenance and discretionary activities, the former is less than unity and the latter is greater than unity. In other words, maintenance activities are a necessity and discretionary activities are a luxury. With respect to the own travel time price elasticities, if the travel time price of performing a certain type of activity increases (for reasons such as traffic congestion), one would reduce the time allocated to that type of activity. Time spent on maintenance activities is less elastic than the time spent on discretionary activities. As for the cross travel time price elasticities (changes in time allocated to activity type i in responses to changes in the time price for activity type j), we found that ɛ_dm>0 and ɛ_md>0, suggesting a substitution effect between maintenance and discretionary activities.     

Short-term impact analysis of fuel price policy change on travel demand in Malaysian cities

Abstract

Malaysia is one of the few countries in the world that provides a fuel subsidy to consumers. Due to the recent economic crisis, the Malaysian Government decided to revise its fuel subsidization policy from a fixed price subsidy to a floating price subsidy dependent on global oil demand. Recognizing that the change in fuel subsidization policy can have an impact on travel behavior, this article investigates the short-term impact of the policy change on private and public transportation in the Klang Valley region of Malaysia. Spectral analyses are performed to investigate if the policy change has an impact on private vehicle travel demand, measured in terms of road traffic, and short-term travel demand elasticity with respect to fuel price is estimated. To measure the impact on the public transportation system, the demand cross-elasticity values of rail transit and buses are also estimated. It was found that traffic flow reduces with an increase in fuel price, although elasticity and cross-elasticity values obtained are low. The article finds that there is a potential mode shift from private vehicles to rail transit with increasing fuel price. It is demonstrated that reducing fuel price subsidy can be an effective travel demand management strategy to alleviate congestion.     

Biofuel refinery location and supply chain planning under traffic congestion

This research focuses on planning biofuel refinery locations where the total system cost for refinery investment, feedstock and product transportation and public travel is minimized. Shipment routing of both feedstock and product in the biofuel supply chain and the resulting traffic congestion impact are incorporated into the model to decide optimal locations of biofuel refineries. A Lagrangian relaxation based heuristic algorithm is introduced to obtain near-optimum feasible solutions efficiently. To further improve optimality, a branch-and-bound framework (with linear programming relaxation and Lagrangian relaxation bounding procedures) is developed. Numerical experiments with several testing examples demonstrate that the proposed algorithms solve the problem effectively. An empirical Illinois case study and a series of sensitivity analyses are conducted to show the effects of highway congestion on refinery location design and total system costs.     

A recursive, cell processing model for predicting freeway travel times

Travel time information influences driver behaviour and can contribute to reducing congestion and improving network efficiency. Consequently many road authorities disseminate travel time information on road side signs, web sites and radio traffic broadcasts. Operational systems commonly rely on speed data obtained from inductive loop detectors and estimate travel times using simple algorithms that are known to provide poor predictions particularly on either side of the peak period. This paper presents a new macroscopic model for predicting freeway travel times which overcomes the limitations of operational ‘instantaneous’ speed models by drawing on queuing theory to model the processing of vehicles in sections or cells of the freeway. The model draws on real-time speed, flow and occupancy data and is formulated to accommodate varying geometric conditions, the relative distribution of vehicles along the freeway, variations in speed limits, the impact of ramp flows and fixed or transient bottlenecks. Field validation of the new algorithm was undertaken using data from two operational freeways in Melbourne, Australia. Consistent with the results of simulation testing, the validation confirmed that the recursive model provided a substantial improvement in travel time predictions when compared to the model currently used to provide real-time travel time information to motorists in Melbourne.