Transportation Research Record: Journal of the Transportation Research Board

This study seeks smart credit-based congestion pricing (CBCP) solutions for maximally improving travelers’ welfare by varying toll levels and locations across the Austin, Texas network. Scenarios evaluated include selecting links with maximum delays by variably tolling bridges and by recognizing congestion externalities across all links. Travel demand models deliver inputs for normalized logsum differences to quantify and compare consumer surplus changes across traveler types, around the region. Results suggest limited tolling locations under four broad times of day can do more harm than good, unless travelers shift out of the PM and AM peak periods or revenues are returned to travelers as credits. When using CBCP across all congested links at congested times of day (with 10% of revenues retained to cover system administrative costs), an average net benefit of $1.61 per licensed driver per weekday is estimated, with almost all travelers benefiting. For example, 95% of the traffic analysis zones’ lowest value of travel time (VOTT) group (VOTT1 = $5/hour) are expected to benefit from the CBCP policy. Tolling at twice the difference between marginal social cost and average travel cost (on each subset of congested links) appears to benefit more people, although tolling high on various links adds to congestion elsewhere. For example: tolling Austin’s highest-delay-producing or “top 500” links is estimated to benefit 98.5% of the zones’ highest VOTT (VOTT5 = $45/hour) travelers, while raising vehicle-miles traveled by just 0.8% (as a result of more circuitous, congestion- and toll-avoiding travel).

Short-term forecasting of metro transit passenger flows is of great importance to the urban subway system in the various aspects of train and crew scheduling, congestion mitigation strategies, operational decision-making, and dynamic information provision. In this paper, a hybrid short-term forecasting approach is developed by combining the modified gravity model and deep learning models (e.g., convolutional neural networks [CNN] with auto-encoder). There are three components in this hybrid forecasting approach: (a) the modified gravity model that incorporates both the geographic information surrounding metro stations and station-level inflows/outflows as regression attributes; (b) the convolutional auto-encoder that tackles the sparsity issues of origin–destination (OD) matrices of passenger flows; and (c) the fusion of physical regression results and the decoder matrix, where the backpropagation algorithm is applied to tune the optimal fusion weight parameter matrix. The combination enables the proposed approach to achieve the trade-off between model interpretability and forecasting accuracy. The proposed approach is applied to the short-term forecasting of passenger flows for the metro transit network in Beijing, China. The experimental results show that the hybrid approach is promising and outperforms the benchmark models, for example, time-series models, long short-term memory, and CNN. The application demonstrates that the proposed hybrid short-term forecasting approach is suitable in both the station-level trip generation/attraction and the inter-station OD passenger flows.

With the continuous increase in interstate highway traffic and demand for higher safety standards, there is a growing need for rapidly scalable road inspection. Currently, inspection and condition assessment of roadways involve manual operations which increase labor costs and limit the scalability and inspection coverage. Furthermore, manually inspecting highways adds additional safety risks for highway workers and road inspectors. To address these challenges, we envision a fully automated process of highway inspection. This paper presents a novel low-power drone-mountable real-time artificial intelligence (AI) framework for road asset classification through visual sensing, which is the first step toward a fully automated inspection system. We analyzed a state DOT dataset, consisting of 14 different kinds of defected road assets. To this end, we developed our baseline framework using MobileNet-V2, which is a convolutional neural network (CNN) specially developed for mobile and embedded platforms. Since our target dataset was small and CNNs networks require a huge amount of data, we leveraged transfer learning, by pretraining MobileNet-V2 using the ImageNet dataset and then fine-tuned it on our target dataset. This new framework was ported to embedded platforms Nvidia Jetson Nano with the capability to perform on-board drone processing. Overall, our results demonstrate 81.33% accuracy on the test set while processing 7.4 frames per second and occupying a total power of 1.9 W. It achieved a Power Reduction Factor (PRF) of 21.17 over Nvidia TitanV implementation, with only 8.74% impact on the projected drone flight time.

The amount of dangerous goods (DG) transported by rail within Canada has increased by an average of approximately 25% since 2004, with a 42.5% increase in transported fuels and chemicals between 2011 and 2017. Further, movement of DG by rail is forecasted to continue increasing. Sustainable growth in the transport of dangerous goods (TDG) by rail will require acceptable safety levels. This study provides insight into key occurrence types for TDG and their causes, to better focus on risk control strategies, including measurement and control of leading and lagging safety indicators. This work also reviews current safety performance and Canadian railway incident occurrence databases. The results of the analyses suggested that the performance against lagging indicators currently being reported is adequate, including derailments and collisions (main and nonmain track), serious injuries (including fatalities), DG leakers, and releases. Furthermore, a list of the rail accidents with the greatest number of fatalities was used to calculate a crude estimate of societal risk associated with rail transportation. According to UK Health and Safety Executive (HSE) recommendations, this analysis indicated that the estimated rail transport risks would be considered acceptable when assessed at a milepost scale. However, there are opportunities for further enhancing safety reporting, management, and performance.

India ranks first in the global fatalities rate related to traffic accidents. India’s trucking industry is highly unorganized compared with that in developed countries, as a result of which drivers do not get professional recognition in society. Low income, job insecurity, high-fatigue conditions, and continuous exposure to external environmental conditions cause job dissatisfaction, and the majority of traffic accidents in India are caused by driver negligence. This study aims to systematically examine the underlying factors that cause drivers’ unsafe behavior from the following four assessment levels: personal, professional, organizational, and external factors. A purposeful sampling-based survey method was employed to collect the data. The findings of this study were compared with the opinions of the experts and results from previous studies in the literature. The study results found that the contribution of professional and organizational factors to unsafe behavior is greatest compared with external and personal factors. The study concludes with recommendations for reducing drivers’ risk through planned driving schedules, ensuring social security through welfare schemes, and improving driving performance through proper training programs for preventing and minimizing damage caused by accidents, and recommends policy-based measures to trucking companies and regulatory bodies for minimizing accident occurrence.

Given that older adults are prone to car cessation, they may also be at risk of food insecurity. Online shopping has the potential to become a key solution to this growing social issue. The objective of this study was to understand how mode use relates to food shopping patterns, and what specifically motivates older adults to choose certain travel modes for grocery shopping or to shop online. Sixty-one retired individuals were interviewed in Montreal, Canada. Participants were first asked to discuss their food shopping habits and the modes they used to purchase food. Then, participants were asked open-ended questions about beliefs from the theory of planned behavior. Participants listed advantages/disadvantages, people who approve/disapprove, and facilitating factors/barriers related to travel modes and online grocery shopping. Most participants never used online grocery shopping. Results revealed similarities in shopping styles between car drivers and online shoppers. Both were organized (prepared lists), shopped in bulk, and went on regularly timed shopping trips. Public transit (PT) and active mode users were spontaneous and irregular shoppers who viewed in-person shopping as physical and social activity opportunities. Grocery shopping using these modes could be made easier for some participants if shops offered home delivery after in-store purchases. Car drivers were more likely to adopt online services than PT or active mode users who preferred delivery after in-person shopping to reduce obstacles linked to these modes. In order for online grocery shopping to be integrated as part of one’s established habits, both travel habits and grocery shopping habits must be observed jointly.

Application of connected and automated vehicles (CAVs) is expected to have a significant impact on traffic safety and mobility. Although several studies evaluated the effectiveness of CAVs in a small roadway segment, there is a lack of studies analyzing the impact of CAVs in a large-scale network by considering both freeways and arterials. Therefore, the objective of this study is to analyze the effectiveness of CAVs at the network level by utilizing both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. Also, the study proposed a new signal control algorithm through V2I technology to elevate the performance of CAVs at intersections. A car-following model named cooperative adaptive cruise control was utilized to approximate the driving behavior of CAVs in the Aimsun Next microsimulation environment. For the testbed, the research team selected Orlando central business district area in Florida, U.S. To this end, the impacts of CAVs were evaluated based on traffic efficiency (e.g., travel time rate [TTR], speed, and average approach delay, etc.) and safety surrogates (e.g., standard deviation of speed, real-time crash-risk models for freeways and arterials, time exposed time-to-collision). The results showed that the application of CAVs reduced TTR significantly compared with the base condition even with the low market penetration level. Also, the proposed signal control algorithm reduced the approach delay for 94% of the total intersections present in the network. Moreover, safety evaluation results showed a significant improvement of traffic safety in the freeways and arterials under CAV conditions with different market penetration rates.

Rural roads are a critical component of the transportation network in the U.S., including Michigan, where county roads comprise of a majority of the state’s roadway mileage. The rates of fatal crashes on rural highways are substantially higher than that on urban roads. Previous research has investigated the safety impacts of driveway density, but the effects of driveway land use on rural roadway safety performance, particularly for county roadways, remains under-researched. This study analyzed the safety impacts of various classifications of driveway land utilization on rural two-lane state and county roads. Non-animal segment crashes from 2011 to 2015 were analyzed along with roadway data for over 5,556 mi of state highways and 5,890 mi of paved county segments from across Michigan. To account for the unobserved heterogeneity associated with varied county design standards and site characteristics, mixed-effects negative binomial regression with county- and site-specific random effects was utilized. Separate models were developed for state highways and paved county roads. The results indicated that commercial driveways possess a stronger effect on crash occurrence than other driveway land use types, including residential and industrial driveways. The effect of driveway density on crash frequency was also found to be stronger on state highways compared with the county roads. This study contributes to the limited body of knowledge in relation to the relationship between traffic safety and driveway land use for rural roadway segments, particularly for county roads, which typically possess design and travel characteristics that are considerably different from those of state highways.

This study addresses the question of parking supply and demand at transit-oriented developments (TODs) through comparative case studies of seven TODs in the U.S.A. As far as the authors can determine, this is one of the first studies to estimate peak parking generation rates for TODs. Developments are often characterized in relation to “D” variables—development density, land use diversity, urban design, destination accessibility and distance to transit. The seven TODs studied in this project are exemplary when it comes to the Ds. At the overall peak hour, just 51.2%–84.0% of parking spaces are filled. Because of limited use of shared parking, even these exemplary developments do not achieve their full potential. At the overall peak hour, parked cars would fill just 19.5%–69.4% of parking spaces if the developments were built to Institute of Transportation Engineers (ITE) standards. With one exception, peak parking demand is less than 60% of the parking supply guideline in the ITE Parking Generation manual. A sixth D, demand management (parking management), is mixed at the TODs studied. For one thing, there is a dearth of shared parking, though opportunities abound. Another area in which parking policies are not always smart is in bundled residential parking. At some TODs, a parking space/permit comes with each apartment whether the renters want it and use it or not. Such parking is effectively free. A third area in which parking policies are not always smart is in free commercial parking, the counterpart of bundled residential parking.

Synthetic population is used in many transport models ranging from trip-based, hybrid trip, tour-based, and activity-based models. As mobility decisions depend on both individuals’ characteristics and family situation, generating a two-layered population that takes into account not only the individual level but also household level is essential. In the literature, three main categories of methods for two-layered population generation have been proposed. These categories are synthetic reconstruction (SR), combinatorial optimization (CO), and statistical learning (SL). SR and CO methods produce synthetic populations by means of replicating individuals, whereas SL methods generate a population following a joint probability estimation. However, selecting a generation process is not straightforward as it depends on input data and synthetic population characteristics. To the best of our knowledge, no clear methodology for selecting between these methods exists. The main objectives of this paper are to provide (1) a detailed description of the available methods, (2) a comparison between these methods, and (3) a decision-making procedure for selecting between these methods. The description and comparison of the methods relies on different criteria: marginals availability, sample size, number of potential attributes that can be handled, population size to generate, possibility of zero-cell problem, and so forth. The advantages and shortcomings of each method are illustrated, and method performance is assessed. The decision-making procedure is carried out through the proposal of a decision tree. Researchers and practitioners have now access to a comprehensive and unified framework to select the appropriate method depending on available data and features of their modeling purposes.

Cross-sectional and the empirical Bayes (EB) before–after are two of the most common methods for estimating crash modification factors (CMFs). The EB before–after method has now been accepted as one way of addressing the potential bias caused by the regression to the mean problem. However, sometimes before–after methods may not feasible because of the lack of data from before and after periods. In those cases, researchers rely on cross-sectional studies to develop CMFs. However, cross-sectional studies may provide biased CMFs through confounding. The propensity score (PS) matching method, along with cross-sectional regression models, is one of the methods that can be used to address confounding. Though PS methods are widely used in epidemiology and other studies, there are only a few studies that have used PS matching methods to estimate CMFs. The intent of this study is to evaluate and compare the performance of cross-sectional regression models using PS matching methods with the results from the EB and traditional cross-sectional methods. The comparisons were conducted using two carefully selected simulated datasets. The results indicate that optimal propensity score distance (PSD) matching with maximum variable ratio of 5 performed quite well compared with the EB before–after and the traditional cross-sectional methods.

On July 31, 2014, a series of gas pipeline explosions occurred in Kaohsiung City, causing 32 deaths and 321 injuries. Following this accident, the Kaohsiung City government decided to replace the use of pipelines with trucks for transporting hazmat, transfering the risk from beneath the road to the surface. This means that careful consideration needs to be given to safety and cost in hazardous material (hazmat) transportation route planning. The issue of how to design optimal routes for the transportation of hazmat is, therefore, important and involves consideration of various criteria. This research focuses on three objectives: cost, risk, and emergency response capability. It then constructs two solution algorithms, the compromise weight model and the evolutionary algorithm, to solve the multi-objective problem. The results from these two algorithms are observed and compared. In addition, this research also provides some recommendations for stakeholders, including the hazmat industry, government, and residents.

Crack sealing is an important preventive treatment in the pavement preservation program. To achieve a cost-effective crack seal, it is crucial to select a proper crack sealing method. In Minnesota asphalt pavement cracks are sealed using both the clean-and-seal and rout-and-seal methods; however, there is no guideline for choosing the most suitable crack sealing method. This study deals with a literature review, an online survey, crack seal performance data collection, cost-effectiveness analysis of the crack sealing methods, life cycle cost analysis, and development of two decision trees to aid in selecting the most suitable crack sealing method. The first, which can be used in the pavement management system, needs information such as crack severity, pavement type (new versus overlay), pavement analysis period and design life, traffic level, and crack seal sequence (first, intermediate, or last). The second decision tree, which is a simplified version of the first and can be used by preventive maintenance crews, requires less information, such as crack severity, traffic level, and place in the crack sealing sequence.

Moisture availability is a crucial factor for ongoing hydration reactions and controlling capillary pore water potential (PWP) development in cement-based mixtures. Harsh exposure conditions (such as low relative humidity, high temperature, or high wind speed) or incorporating fine cementitious materials may increase the mixture susceptibility to PWP development, and thus, early-age shrinkage. This study aims to investigate PWP and electrical conductivity development in cement paste and mortar mixtures with various water-to-cement ratios (from 0.25 to 0.4), silica fume substitutions, and internal curing in both sealed and harsh exposure conditions (ambient temperature from 10 to 35°C and relative humidity from 25% to 75%). The results indicate that high ambient temperature and silica fume incorporation substantially accelerate moisture loss and PWP development. The outcome of this study also shows the capability of internal curing using lightweight fine aggregate to delay PWP development in mixtures containing silica fume or exposure to low relative humidity and high-temperature ambient environments.

This paper summarizes the results of a laboratory testing program that was conducted to determine the effects of moisture content on the shakedown limits of unbound granular base materials. Two different types of granular base materials were investigated in this study, namely limestone and sandstone. Multi-stage repeated load triaxial tests were performed on these materials. The results of the tests were analyzed within the framework of the shakedown theory. The results indicate that the moisture content had an influence on the slope of the elastic and plastic shakedown limits lines. The effect of the moisture content was more pronounced on the slope of the elastic shakedown limit line, however. The moisture content affected the intercept of the elastic and plastic shakedown limits lines more significantly than the slope of these lines. The limestone material exhibited greater decrease in the intercept of the elastic and plastic shakedown limits with increase in moisture content compared with the sandstone material. This was explained by the limestone’s finer gradation.

Currently, joint sealant is designed without consideration of the bond strength between the concrete and the sealant and the effect of shape on stress concentration. This often leads to adhesive failures within 1.5 years, earlier than the expected service life of the joint sealant, which is 20 years. In adhesive failure, the strength of the bond and the stress of the interface between the sealant and the face of the joint reservoir play a very important role. To examine the nature of the bond along the sealant/joint well interface, experimental bond tests were conducted. In addition, the stress distribution on the interface was also investigated according to geometry (Shape Factor [SF] and degree of curvature [DoC]). Re-evaluation of the SF was conducted, and a new design factor, DoC, was introduced and investigated through the finite element method of analysis. With these factors, the reduction of bond strength and increase in the stress at the interface can be limited reducing the potential for early adhesive failure. Based on this study, the effect of joint preparation (dirt and moisture) on joint strength and shape (SF and DoC) of joint sealant should be considered when designing and installing sealants.

Perception-reaction time (PRT) and deceleration rate are two key components in geometric design of highways and streets. Combined with a design speed, they determine the minimum required stopping sight distance (SSD). Current American Association of Highway Transportation Officials (AASHTO) SSD guidance is based on 90th percentile PRT and 10th percentile deceleration rate values from experiments completed in the mid-1990s. These experiments lacked real-world distractions, and so forth. Thus, the values from these experiments may not be applicable in real-world scenarios. This research evaluated (1) differences in PRTs and deceleration rates between crash and near-crash events and (2) developed predictive models for PRT and deceleration rate that could be used for roadway design. This was accomplished using (1) genetic matching (with Rosenbaum’s sensitivity analysis) and (2) quantile regression. These methods were applied to the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) data.

The analysis results indicated that there were differences in PRT and deceleration rates for crash and near-crash events. The specific estimates were that, on average, drivers involved in crash events took 0.487 s longer to react and decelerated at 0.018 g’s (0.58 ft/s²) slower than drivers in equivalent near-crashes. Prediction models were developed for use in roadway design. These models were used to develop tables comparing existing SSD design criteria with SSD criteria based on the results of the predictive models. These predicted values indicated that minimum design SSD values would increase by 10.5–129.2 ft, dependent on the design speed and SSD model used.

This paper investigates the effect of incidents on freeway segment capacity. Currently, the Highway Capacity Manual (HCM) provides a look-up table linking the remaining segment capacity fraction during an incident to the total and closed number of lanes on the segment. In reality, segment capacity during an incident will tend to vary over time, with the most severe effects felt early on before any type of response is initiated, with congestion progressively improving as the appropriate incident management actions are implemented. By applying a genetic algorithm calibration method on each incident day and calibrating the incident capacity adjustment factors (CAFs), optimal time-dependent CAFs were derived that best represented the effect of incidents on the freeway segment capacity. By analyzing the optimal CAFs, the strongest relationship was revealed to be between the optimal time-dependent CAF and the temporal progression of the incident. A regression model was developed to represent this behavior. This was formulated in a manner that can directly adjust the current HCM’s fixed CAF values (for a specific lane closure configuration) for modeling incidents both in single day, seed file application, or for an entire year reliability analysis. A portion of WB I-540 in Raleigh, NC was selected as the study area in which the proposed method was tested. Between January 2014 and December 2018, the team identified 22 isolated incidents (away from the recurring congestion period) that closed one or two lanes of traffic, creating a distinct congestion pattern.

Bonded concrete overlay of asphalt (BCOA), previously known as ultra-thin whitetopping (UTW), has been widely used to repair aged asphalt concrete (AC) pavements with moderate distresses. Because of the increasing costs of roadway maintenance, Louisiana has a great interest in determining whether thin BCOA (usually 2–6 in.) is a suitable and cost-effective alternative to the current practice of roadway maintenance. The objective of the study was to evaluate the performance of BCOA pavement and to identify the influence of in-situ interface bond strength on the performance of BCOA pavements. Three full-scale BCOA test sections with thicknesses of 6 in., 4 in., and 2 in. of Portland cement concrete (PCC) over an aged asphalt pavement were tested under accelerated pavement test (APT) loading under typical pavement conditions in southern Louisiana. Each section was trafficking-loaded to a failure (i.e., all the slabs in the loading path were cracked) under alternating load magnitudes of 9 kips and 16 kips of the ATLaS dual-tire wheel load. A falling weight deflectometer (FWD) backcalculated the effective thickness, a trench-cutting investigation was undertaken, and in-situ pull-off test revealed that a good bond was established initially between the PCC and AC layer. Several non-destructive test (NDT) methods indicated that the distresses of a BCOA slab could be coupled with a possible debonding at the PCC-asphalt interface. This paper mainly focuses on the APT results and the performance of BCOA test sections with different overlay thickness.

Accidents rank third among the top 10 leading causes of death in Louisiana, claiming more than 2,000 lives out of a total of almost 33,000 deaths. Drivers’ characteristics (age and gender), the geometry of the roadways, driving on the major roadways, the day of the week, and the wet or dry condition or the road have been associated with crash severity. This study applies unordered multinomial logistic models to investigate causes leading to crash severity in Louisiana. Several models were estimated and the best results were retained for presentation and discussion. Consistent with previous research, findings suggest that drivers’ gender and age matter for traffic safety. Individually, male and older drivers are too risky. Major roads, weekdays, dry surfaces, and road geometry increase the risk of fatal accidents. Male drivers are prone to severe and fatal accidents while old drivers are vulnerable to all types of accidents. Young drivers and female drivers feature among cases of injury and moderate accidents. Evidence suggests that crash severity is not ethnicity specific, contrary to some studies. This study is relevant because it builds a new dataset for safety research, identifies risk factors, and informs the aim of public safety policy to reduce loss of life, injuries, and costs resulting from motor vehicle accidents.

Vehicle fleet electrification is regarded as one major pathway toward achieving energy independence and reducing air pollution and greenhouse gas emissions. Compared to light-duty and medium-duty vehicles, electrification of heavy-duty vehicles, especially Class 8 trucks, is more challenging owing to the battery size required to attain the driving range necessary for their operating goals. As drayage trucks generally have a limited daily mileage, return to a home base every night, and spend a large amount of time creeping and idling, drayage operation has been the first targeted application for Class 8 electric trucks. The feasibility of operating battery electric drayage trucks at the individual vehicle level has recently been demonstrated. However, questions remain as to whether these trucks are capable of meeting the needs of typical drayage operation at the fleet level. This paper presents a feasibility analysis of operating an electric truck fleet based on real-world operation data of a diesel drayage operator in Southern California. Second-by-second activity data collected from 20 trucks in the fleet were used to estimate the corresponding electric energy consumption and the state of charge of the battery using a microscopic electric energy consumption model. An algorithm for generating tours of drayage activity from the collected data was developed and implemented. Multiple scenarios with different battery charging and truck scheduling assumptions were analyzed. The results show that 85% of the tours could be served by electric trucks if there is opportunity for charging at the home base during the time gap between consecutive tours.

Based on full-scale field trials, this study investigated the mechanical properties of geocell-reinforced asphalt pavements. The field site is a four-lane highway in Hubei province in China. The dynamic load tests were performed for the geocell-reinforced and unreinforced pavement under three different conditions of axle loads and running speeds. The static load tests were carried out under three different axle loads which were the same as the dynamic load tests. The dynamic and static responses were studied in relation to the peak of the longitudinal strain, creep strain, and the improvement factors. Field trial results revealed that the longitudinal strain peak value of the pavement structure reinforced by Polypropylene geocell was significantly reduced in comparison with the pavement response without geocell. In addition, the reinforcement system had a certain inhibiting effect on the creep strain and the improvement factor increased with increasing axle load and decreased with increasing speed. Under 100 kN axle load, the improvement factors were 0.78, 0.68, 0.54, and 0.39 at speeds of 0, 20, 40, and 60 km/h. Under 150 kN axle load, the improvement factors were 0.96, 0.89, 0.75, and 0.61 at speeds of 0, 20, 40, and 60 km/h. Under 200 kN axle load, the improvement factors are 1.21, 1.1, 0.97, and 0.79 at speeds of 0, 20, 40, and 60 km/h. This research helps to provide a new and effective method of asphalt pavement reinforcement.

The recent growth in the California hydrogen fuel cell vehicle (FCV) market offers the opportunity to analyze how refueling stations that drivers use after some experience compare with those they initially intended to use. Online surveys completed by 124 FCV adopters in California in early 2019 were analyzed. Respondents listed stations they initially planned to use, stations that they later used, subjective reasons for using them, and important travel destinations. Network GIS analysis was then used to measure estimated travel times between both available and planned retail hydrogen stations and home, work, and frequently traveled routes, both at the time of adoption and at the time of the survey. Results show that 40% of respondents changed refueling stations over time. Those with stations objectively nearer to home, work, and frequently traveled routes were less likely to change their list. Drivers were more likely to subjectively label stations as near home and less likely to label them as on the way compared with objective measurements of these criteria, though these differences are greater for respondents who changed stations. Regardless of whether the station was available pre-adoption or opened post-adoption, stations that respondents added to their lists were farther from home than those they initially intended to use. For stations available pre-adoption, reliability positively influenced adding them after experience, while stations added by drivers that opened post-adoption tended to require short deviations to reach. These results indicate that a mixture of geographic and station-level characteristics contribute to FCV drivers changing stations over time.

This paper aims to estimate short-term transportation demand fluctuations because of events such as meteorological events, major activities, and subway service disruptions. Four different modes are analyzed and compared, being bikesharing, taxi, subway, and bus. Case study includes 3 years of transactional data on working days collected in Montreal, Canada. Generalized additive models (GAM) are developed for every mode. The dependent variable is the hourly number of trip departures from one subway station neighborhood. Independent variables are data from various events. Different models are calibrated for every subway station neighborhood to better understand spatial differences. Also, performance of GAM and autoregressive integrated moving average models are compared for prediction on different horizons. Results suggest that presence of rain decreases bikesharing, subway, and bus demand, while increasing taxi demand. In fact, after four consecutive hours of rain, bikesharing demand decreases by 28.0%, subway and bus demand decreases by 4.6%, while taxi increases by 13.9%. Wind is only found significant for bikesharing. Temperature is found significant for all four modes but has a larger effect on bikesharing and taxi. Moreover, demand increases significantly during subway service disruptions for the three alternative modes studied, especially for taxi, suggesting an increase in demand of 182% during disruptions of 1 h. Furthermore, activities influence demand for all four modes, but subway seems to be the most affected one. This method allows for a better understanding of travel behaviors and makes it possible to consider a more dynamic adaptation of the transportation service supply to match travel demand based on various events. This could lead to better co-planning of events and transportation service, for example by temporarily increasing subway frequency or changing the position of some bikesharing stations.

This study analyzed whole-body vibration (WBV) on a car seat (seat surface and feet) in passenger cars as a function of longitudinal road roughness. Measurements were provided on nine different cars in six categories and included a total travel distance of 1,860 km. The root mean square (RMS) of the frequency-weighted acceleration was used to quantify WBV. The relationship between seat acceleration response and comfort reactions according to the ISO 2631-1 and the International Roughness Index (IRI) was estimated. IRI thresholds were proposed as a function of vehicle speed and road category. Proposed IRI thresholds decreased with vehicle velocity and were similar with published IRI threshold proposals based on simulation. IRI thresholds as a function of speed limit should decrease with power by approximately –0.75. Substantially lower (by ~ 40%) IRI thresholds were calculated for the total vibration value (six signals) in comparison with vertical vibration on the seat surface.

Although there exists a well-studied relationship between parking policies and automobile demand, conventional practices evaluating the transportation impacts of new land development tend to ignore this. In this paper, we: (a) explore literature linking parking policies and vehicle use (including vehicle trip generation, vehicle miles traveled [VMT], and trip length) through the lens of development-level evaluations (e.g., transportation impact analyses [TIA]); (b) develop a conceptual map linking development-level parking characteristics and vehicle use outcomes based on previously supported theory and frameworks; and (c) evaluate and discuss the conventional approach to identify the steps needed to operationalize this link, specifically for residential development. Our findings indicate a significant and noteworthy dearth of studies incorporating parking constraints into travel behavior studies—including, but not limited to: parking supply, costs or pricing, and travel demand management strategies such as the impacts of (un)bundled parking in housing costs. Disregarding parking in TIAs ignores a significant indicator in automobile use. Further, unconstrained parking may encourage increases in car ownership, vehicle trips, and VMT in areas with robust alternative-mode networks and accessibility, thus creating greater demand for vehicle travel than would otherwise occur. The conceptual map offers a means for operationalizing the links between: the built environment; socio-economic and demographic characteristics; fixed and variable travel costs; and vehicle use. Implications for practice and future research are explored.

Aircraft valuation and the estimation of an accurate aircraft price is undoubtedly a challenging task that has significant consequences for airlines. This paper presents an asset valuation model to show how a series of endogenous as well as exogenous factors can influence the value of an aircraft. Specifically, a discounted cash flow methodology is used to forecast the valuation of an old or new generation aircraft. Both total operating revenue and aircraft operating costs are taken into account to devise a reliable pre-tax profit measurement that is used as the basis of the discounted cash flow analysis. A sensitivity analysis based on Monte Carlo simulation is utilized to identify which factors have a more significant influence on the suggested aircraft value. Therefore, it addresses how value fluctuates in response to economic fluctuations. Indeed, the calculated value of an aircraft highly depends on the underlying assumptions used. The calculated value is compared with available data in a case study for verification.

This paper presents a behavioral car following model, named the chained asymmetric behavior model, that improves on the asymmetric behavior model. This model is inspired by the empirical observation that vehicles react proportionately to the magnitude of disturbance experienced when traversing through a stop-and-go oscillation, deviating from a constant following behavior observed in equilibrium conditions. Findings from simulation experiments suggest that this “second-order” effect significantly affects traffic throughput and evolution under disturbances. Knowledge obtained from the model is leveraged toward designing control for connected automated vehicles in mixed traffic streams.

Understanding how vehicle drivers and pedestrians interact is key to identifying countermeasures that improve the safety of the interactions. As a result, techniques that can be used to evaluate the effectiveness of safety countermeasures and traffic control devices without the need to wait for the availability of crash data are needed. Using video, the interactions between right-turning vehicles and conflicting pedestrians were documented and quantified using vehicle and pedestrian position timestamps. Interactions documented were purposely narrow in scope to obtain a controlled dataset. Logged timestamps enabled the calculation of values such as time to complete a right turn and time for a pedestrian to reach a critical conflict point when a vehicle initiated a right turn. A nonprobabilistic regression model explaining the relationship between the calculated values was created. The model described the expected behavior of right-turning drivers: when drivers perceive the possibility of a pedestrian reaching a critical conflict point at the same time as them, they will modify their behavior, even if not coming to a complete stop. This behavior is not a surprise and has been previously documented in the literature. The primary contribution of this research is demonstrating that by analyzing a narrow set of interactions, clear and simple models that mostly explain the interactions between right-turning vehicles and pedestrians can be developed using nonprobabilistic linear regression techniques. An argument is made that the model parameters can be used to evaluate the effectiveness of traffic control devices.