Incorporating attitudinal aspects in roadway pricing analysis

Jin, Xia; Hossan, Sakoat; Asgari, Hamidreza; Shams, Kollol

doi:10.1016/j.tranpol.2017.03.020

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…For instance, for same-direction crashes (K = 1), four blocks showed significant degrees of homogeneity (12e kl ). Blocks with K/L = {(1, 2), (1,4), (1,7), (1,8)} showed more than 90% degree of homogeneity. The associated subset of attributes in these blocks (columns 2, 4, 7, and 8) can be obtained from column clustering result (Table 4) to describe this type of crashes.…”

Section: Block Clusters (Both Attribute and Crash Clustering)mentioning

confidence: 99%

“…In other words, the impacts of specific factors might vary among the observations, leading to random distribution of parameter coefficients rather than fixed impacts. Presence of heterogeneity in different aspects of travel behavior and traffic data has been widely discussed ( 6 , 7 ). Traffic accident data are often heterogeneous considering that the occurrence and severity of a crash is the result of multiple contributing factors at the same time ( 8 ).…”

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confidence: 99%

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Clustering Approach toward Large Truck Crash Analysis

Rahimi

Azimi

Asgari

et al. 2019

Transportation Research Record: Journal of the Transportation R

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Heterogeneity of crash data masks the underlying crash patterns and perplexes crash analysis. This paper aims to explore an advanced high-dimensional clustering approach to investigate heterogeneity in large datasets. Detailed records of crashes involving large trucks occurring in the state of Florida between 2007 and 2016 were examined to identify truck crash patterns and significant conditions contributing to the patterns. The block clustering method was applied to more than 220,000 crash records with nearly 200 attributes. The analysis showed promising results in segmenting a large heterogeneous dataset into meaningful subgroups (with 95.72% average degree of homogeneity for selected blocks). The goodness of fit for clustering methods is evaluated and both integrated completed likelihood (ICL) and pseudo-likelihood values improved significantly (20.8% and 21.1% respectively). Attribute clustering showed distinct characteristics for each cluster. Crash clustering revealed significant differences among the clusters and suggested that this crash dataset could be portioned as same-direction, opposing-direction, and single-vehicle crashes. Individual blocks defined by both row and column clustering were further investigated to better understand the contribution set of conditions that lead to large truck crashes. Major features for each of the three major types of crashes were analyzed, which may provide additional insights to develop potential countermeasures and strategies that target specific segments. The clustering approach could be used as a preanalysis method to identify homogeneous subgroups for further analysis, which will help enhance the effectiveness of safety programs.

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Section: Block Clusters (Both Attribute and Crash Clustering)mentioning

confidence: 99%

mentioning

confidence: 99%

Clustering Approach toward Large Truck Crash Analysis

Rahimi

Azimi

Asgari

et al. 2019

Transportation Research Record: Journal of the Transportation R

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“…The attitudinal scale methods are constructed based on the score assigned to multiple statements using a certain scale of values [35]. Using these methods, the risk attitude is considered a latent construct (a dependent variable) that is not directly observable, but can be estimated through other explanatory variables that are related to the statements proposed by different researchers, depending on the topic of study.…”

Section: Theoretical Framework Of the Stated Risk Analysismentioning

confidence: 99%

Analysis of Farmers’ Stated Risk Using Lotteries and Their Perceptions of Climate Change in the Northwest of Mexico

Kallas

Herrera

2018

Agronomy

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Risk attitudes are relevant factors affecting production, management and investment decisions at the farm level. They are key factors related to farmers’ attitudes towards the environment and climate change. Several methodological approaches, which were considered to be preferable for measuring the level of risk of an economic agent, ranging from highly risk-tolerant to highly risk-averse attitudes, are available. The Multiple Price List (MPL) method is one of the stated approaches that is gaining relevance. In this study, we apply the MPL and relate the risk outcomes to farmers’ socio-economic characteristics and their perceptions of the environment and climate change. Data were collected using a face-to-face survey, carried out with a group of 370 farmers of an irrigation district, located in the northwest of Mexico. The results showed a risk level of about 0.32, according to the Constant Relative Risk Aversion (CRRA) coefficient, locating farmers of the region in a risk-averse group. The heterogeneity analysis showed that the socioeconomic factors and the perceptions of climate change are related to the farmers´ stated risk level. Farmers who are young women, with a tendency to use public support for structural investment, were shown to be risk-tolerant. Farmers considered floods, hail, diseases, pests, and weed growth incidences to be the most frequent weather patterns in the region.

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“…Different traffic patterns were identified for modeling to represent different demands and congestion levels. Then, the traffic network and demand parameters (supply and demand) of the simulation-based DTA models were calibrated for different demand levels utilizingreal-world measures such as traffic flow, on each link and on each turning movement (Jin, Hossan, Asgari, & Shams, 2018). The trip matrices were estimated for 15 minutes' time intervals for input to the DTA models.…”

Section: Methodsmentioning

confidence: 99%

Providing optimal dynamic model of traffic lights in super saturated conditions with emphasis on ATM

Rahimi

Zoghi

Monajem

2020

Nexo Revista Científica

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Dynamic signal control strategies are effective in relieving congestions during nontypical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility the midblock incident location as estimated by the first model measures.

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Incorporating attitudinal aspects in roadway pricing analysis

Cited by 11 publications

References 14 publications

Clustering Approach toward Large Truck Crash Analysis

Clustering Approach toward Large Truck Crash Analysis

Analysis of Farmers’ Stated Risk Using Lotteries and Their Perceptions of Climate Change in the Northwest of Mexico

Providing optimal dynamic model of traffic lights in super saturated conditions with emphasis on ATM

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