Ahmad Tavassoli

Evaluation of effects from sample-size origin-destination estimation using smart card fare data

Public transport planners are required to make decisions on transport infrastructure and services worth billions of dollars. The decision-making process for transport planning needs to be informed, accountable, and founded on comprehensive, current, and reliable data. One of the major issues affecting the accuracy of the estimated origin-destination (O-D) matrices is sample size. Cost, time, precision, and biases are some issues associated with sample size. Smart card data can potentially provide much information based on better understanding and assessment of the sample size impact on the estimated O-D matrices. This paper uses South East Queensland (SEQ) data to study the effect of different data sample sizes on the accuracy level of the generated public transport O-D matrices and to quantify the sample size required for a certain level of accuracy. As a result, the total number of O-D trips for the whole network can be accurately estimated at all levels of sample sizes. However, a wide distribution of O-D trips appeared at different sample sizes. The large difference from the actual distribution at 100% sample size was readily captured at small sample sizes where more O-D pairs were not representative. The wide distribution of O-D trips at different levels of sample sizes caused significant errors even at large sample sizes. The variation of the errors within the same sample was also captured as a result of the 80 iterations for each sample size. It is concluded that three major parameters (distribution, number, and sample size of selected stations) have a significant impact on the estimated O-D matrices. These results can be also reflected on the sample size of the traditional O-D estimation methods, such household travel surveys.

Traditional Approaches to Traffic Safety Evaluation (TSE): Application Challenges and Future Directions

Traditional TSE approaches, based on chronological data including classical before-after studies, mainstream/statistical modelling and/or person judgment based approaches, has a long history. Despite the robustness of this established technique, this has not always been appropriate, as crash statistics are frequently questioned. Poor timeline, ethical issue, biasness and misjudgment also critical issues regarding this. For a more qualified and comprehensive form of safety analysis, there is a need for scientific methods that yield valid and reliable safety measures without the need for (or in addition to) accident data. At the very outset of this process, there is a need to pinpoint the confines of the current practices. This paper presents a critical review of state-of-the-art traditional approaches of TSE. The strengths and application challenges of those approaches which are also assessed based on methodological, as well as empirical grounds. Finally, future directions for traffic safety evaluation practices and researches are outlined.

Application of smart card data in validating a large-scale multi-modal transit assignment model

The accuracy of transit assignment plays an important role in the successful design and operation of a transit system. The majority of previous studies on validating transit assignment models has used limited survey data or has lacked a large-scale multimodal and high quality dataset. Considering the advantages of smart card [automatic fare collection (AFC)] systems, the aims of this study are to put forward a methodological framework to validate existing transit assignment models and to quantify the performance of these models. Our study combines data from three sources: the General Transit Feed Specification, an AFC system, and a strategic transport model from a large-scale multimodal public transport network, namely the South-East Queensland (SEQ) network in Australia. The AFC system in SEQ has provided a very large and highly accurate dataset on passenger boardings and alightings for the three transit modes of bus, rail and ferry. Following a data analysis, an origin–destination trip matrix is estimated for the AM peak period using AFC data as an input to the transit assignment model. Then, the results of the transit assignment model are compared with the actual passengers’ route choices over the same period, at different levels of aggregation. The model performance is quantified by each route (and direction), by each segment of each route (and direction), and by each stop. The results indicate that relatively tighter thresholds are required to validate the transit assignment at the segment level than at the stop level. Furthermore, the validation results indicate that the greatest error is realized for the bus mode, while the level of accuracy in the rail mode is the best. The results suggest a segment-level analysis should be used as the most useful level of aggregation for future calibration and validation of transit assignment models.

Application of Traffic Conflict Techniques as Surrogate Safety Measures: A Sustainable Solution for Developing Countries

Social, economic and infrastructure losses due to road traffic accidents and their consequences are very significant all over the world, particularly in developing countries. The evaluation of causative factors of accidents and the selection of remedial measures continues to be based mainly on traditional approaches. Whereas, accident statistics are frequently questioned due to large underreporting of accidents, injuries and property damages, coupled with incomplete and inconsistent recording of information on reported accidents. Poor timelines, ethical issues, biasness and human error are also critical issues. This paper present a comprehensive assessment of the data quality of reported accident databases, in terms of the degree and diversity of the reporting and recording inconsistency, using a case study from Bangladesh.

Determining Effective Sample Size to Calibrate a Transit Assignment Model: A Bayesian Perspective

A transit assignment model is used to predict passenger loads in order to evaluate existing and future transit network scenarios. One fundamental issue affecting the calibration of a transit assignment model is the amount of data required. The present study is designed to determine the effect of different sample sizes on the accuracy level of the estimated passenger flow. A Bayesian model is adapted for transit assignment, and the sample size for three types of priors, namely: uninformative, informative, and overly informative, are examined. In order to assess the value of prior information on passenger flow, the root-mean-square error (RMSE) between each posterior estimate and the actual observation is computed. The posterior estimate that minimizes the %RMSE defines the effective sample size (ESS). This paper uses one day’s automatic fare collection data from the South East Queensland (Australia) transit network to evaluate the effect of sample size and prior information on the posterior passenger flow estimates. The results show that it is not possible to determine the ESS for the Bayesian model with an uninformative prior. With an informative prior, the ESS is 50% of the population and, for the model with an overly informative prior, the ESS is 10% of the population. This means that the lack of prior information cannot simply be compensated by increasing the sample size in this Bayesian model. However, good prior information reduces the necessary sample size substantially.