scholarly journals The Response of Urban Travel Mode Choice to Parking Fees considering Travel Time Variability

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ling Ding ◽  
Xu Yang
Keyword(s):  
Travel Time ◽  
Mode Choice ◽  
Time Variability ◽  
Pricing Strategies ◽  
Travel Time Reliability ◽  
Travel Mode ◽  
Multinomial Logit Models ◽  
Travel Time Savings ◽  
Automobile Use ◽  
Travel Time Variability

Increasing automobile use leads to higher costs for traveling associated with emissions, congestion, noise, and other impacts. One option to address this is to introduce high parking charges to reduce the demand for automobile use and encourage the travel mode switch to public transport. To estimate commuters’ mode choice behavior in response to high parking fees, commuters from Nanjing completed an individually customized discrete choice survey in which they chose between driving and taking the bus or metro when choices varied in terms of time and cost attributes. Multinomial logit models were used to estimate commuters’ responses to high parking charges. In the models, the variability of travel times is considered and analyzed in the stated mode choice models. The results suggest that increases in costs of driving will lead to a great reduction in driving demand. The travel time reliability ratio is 0.50 and the value of each minute late is almost 5.0 times more than the average travel time with the restriction of the maximum allowed delays. The methods used in this study could be adopted to estimate the effect of variable pricing strategies on mode choice responses for different trip purposes. The high value given to travel time variability has implications for transport policy in terms of decision making with respect to new pricing strategies. Moreover, the valuation of travel time savings taken into account in this study would be helpful to better understand the effect of high parking fees.

scholarly journals Analysis of Component Errors in the Highway Capacity Manual Travel Time Reliability Estimations for Urban Streets

2020 ◽  
Vol 2674 (6) ◽  
pp. 85-97 ◽  
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Estimate Travel Time ◽  
The Difference ◽  
Travel Time Variability

The Highway Capacity Manual 6th edition (HCM6) includes a new methodology to estimate and predict the distribution of average travel times (TTD) for urban streets. The TTD can then be used to estimate travel time reliability (TTR) metrics. Previous research on a 0.5-mi testbed showed statistically significant differences between the HCM6 estimated TTD and the corresponding empirical TTD. The difference in average travel time was 4 s that, while statistically significant, is not important from a practical perspective. More importantly, the TTD variance was underestimated by 70%. In other words, the HCM6 results reflected a more reliable testbed than field measurement. This paper expands the analysis on a longer testbed. It identifies the sources and magnitude of travel time variability that contribute to the HCM6 error. Understanding the potential sources of error, and their quantitative values, are the first steps in improving the HCM6 model to better reflect actual conditions. Empirical Bluetooth travel times were collected on a 1.16-mi testbed in Lincoln, Nebraska. The HCM6 methodology was used to model the testbed, and the estimated TTD by source of travel time variability was compared statistically to the corresponding empirical TTD. It was found that the HCM6 underestimated the TTD variability on the longer testbed by 67%. The demand component, missing variable(s), or both, which were not explicitly considered in the HCM6, were found to be the main source of the error in the HCM6 TTD. A focus on the demand estimators as the first step in improving the HCM6 TTR model was recommended.

Traffic and Transit Travel Time Reliability Indexes and Confidence Intervals

2017 ◽  
Vol 2649 (1) ◽  
pp. 28-41 ◽  
Author(s):  
Travis B. Glick ◽  
Miguel A. Figliozzi
Keyword(s):  
Travel Time ◽  
Confidence Intervals ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Reliability Measures ◽  
Time Space ◽  
Estimate Travel Time ◽  
Low Performance ◽  
Transit Reliability ◽  
Travel Time Variability

As congestion worsens, the importance of rigorous methodologies to estimate travel time reliability increases. Exploiting fine-granularity transit GPS data, this research proposes a novel method to estimate travel time percentiles and confidence intervals. Novel transit reliability measures based on travel time percentiles are proposed to identify and rank low-performance hot spots; the proposed reliability measures can be utilized to distinguish peak-hour low performance from whole-day low performance. As a case study, the methodology is applied to a bus transit corridor in Portland, Oregon. Time–space speed profiles, heat maps, and visualizations are employed to highlight sections and intersections with high travel time variability and low transit performance. Segment and intersection travel time reliability are contrasted against analytical delay formulas at intersections—with positive results. If bus stop delays are removed, this methodology can also be applied to estimate regular traffic travel time variability.

Quantile Regression Analysis of Transit Travel Time Reliability with Automatic Vehicle Location and Farecard Data

2017 ◽  
Vol 2652 (1) ◽  
pp. 19-29 ◽  
Author(s):  
Zhenliang Ma ◽  
Sicong Zhu ◽  
Haris N. Koutsopoulos ◽  
Luis Ferreira
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Central Tendency ◽  
Automatic Vehicle Location ◽  
Conditional Mean ◽  
Vehicle Location ◽  
Travel Time Variability

Transit agencies increasingly deploy planning strategies to improve service reliability and real-time operational control to mitigate the effects of travel time variability. The design of such strategies can benefit from a better understanding of the underlying causes of travel time variability. Despite a significant body of research on the topic, findings remain influenced by the approach used to analyze the data. Most studies use linear regression to characterize the relationship between travel time reliability and covariates in the context of central tendency. However, in many planning applications, the actual distribution of travel time and how it is affected by various factors is of interest, not just the condition mean. This paper describes a quantile regression approach to analyzing the impacts of the underlying determinants on the distribution of travel times rather than its central tendency, using supply and demand data from automatic vehicle location and farecard systems collected in Brisbane, Australia. Case studies revealed that the quantile regression model provides more indicative information than does the conditional mean regression method. Moreover, most of the coefficients estimated from quantile regression are significantly different from the conditional mean–based regression model in terms of coefficient values, signs, and significance levels. The findings provide information related to the impacts of planning, operational, and environmental factors on speed and its variability. On the basis of this information, transit designers and planners can design targeted strategies to improve travel time reliability effectively and efficiently.

Travel-Time Reliability as a Measure of Service

2003 ◽  
Vol 1855 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Chao Chen ◽  
Alexander Skabardonis ◽  
Pravin Varaiya
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Average Travel Time ◽  
Travel Time Variability

Statistics from a corridor along Interstate 5 in Los Angeles show that average travel time and travel-time variability are meaningful measures of freeway performance. Variability of travel time is an important measure of service quality for travelers. Travel time can be used to quantify the effect of incidents, and incident information can help reduce travel-time uncertainty. Predictability of travel time is a measure of the benefits of intelligent transportation systems. These measures differ from those defined in the Highway Capacity Manual and other aggregate measures of delay.

Road Pricing under Travel Time Variability

2019 ◽  
Author(s):  
Gege Jiang ◽  
Hong Kam LO ◽  
Zheng LIANG
Keyword(s):  
Travel Time ◽  
Road Pricing ◽  
Time Variability ◽  
Travel Time Variability

scholarly journals Measuring Recurrent and Nonrecurrent Traffic Congestion

2003 ◽  
Vol 1856 (1) ◽  
pp. 118-124 ◽  
Author(s):  
Alexander Skabardonis ◽  
Pravin Varaiya ◽  
Karl F. Petty
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
San Francisco ◽  
Traffic Congestion ◽  
Real Life ◽  
Time Variability ◽  
Loop Detectors ◽  
Bay Area ◽  
Freeway Corridors ◽  
Travel Time Variability

A methodology and its application to measure total, recurrent, and nonrecurrent (incident related) delay on urban freeways are described. The methodology used data from loop detectors and calculated the average and the probability distribution of delays. Application of the methodology to two real-life freeway corridors in Los Angeles, California, and one in the San Francisco, California, Bay Area, indicated that reliable measurement of congestion also should provide measures of uncertainty in congestion. In the three applications, incident-related delay was found to be 13% to 30% of the total congestion delay during peak periods. The methodology also quantified the congestion impacts on travel time and travel time variability.

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