Aggregation and travel time calculation over large scale traffic networks: An empiric study on the Grenoble City

In this paper we consider aggregation technique to reduce the complexity of large-scale traffic network. In particular, we consider the city of Grenoble and show that, by clustering adjacent sections based on a similarity of speed condition, it is possible to cut down the complexity of the network without loosing crucial and intrinsic information. To this end, we consider travel time computation as a metric of comparison between the original graph and the reduced one: for each cluster we define four attributes (average speed, primary and secondary length and heading) and show that, in case of an aggregation rate of 95%, these attributes are sufficient in order to maintain the travel time error below the 25%.     

Over-reporting vs. overreacting: Commuters’ perceptions of travel times

In a large-scale, real-life peak avoidance experiment, we asked participants to provide estimates of their average in-vehicle travel time during their morning commute. After comparing the reported travel times with the actual corresponding travel times, we found that the average travel times were overstated by a factor of 1.5. We showed that driver- and link-specific characteristics partially explained these exaggerations. Using the stated and revealed preference data, we investigated whether the driver-specific reporting errors were consistent with the drivers’ scheduling behaviors in reality and in hypothetical choice experiments. In both cases, we found no robust evidence that drivers behave as if they misperceive travel times to a similar extent as those they misreported, thereby implying that the reported travel times did not represent the actual or perceived travel times in a truthful manner. The results of this study suggest that caution should be recommended when reported travel time data are used in an uncritical manner during transport research and when determining policy.     

紧急救援车辆实时行程时间研究

以分析紧急救援车辆行驶特性、行程时间影响因素为基础，运用波动理论建立紧急救援车辆实时路段行程时间预测模型，给出模型的标定方法，结合实际数据标定模型参数、检验模型精度。     

Growing patronage - Challenges and what has been found to work

This paper synthesizes evidence on growing public transport patronage. The paper firstly examines barriers to patronage growth before reviewing evidence on endogenous factors (those within the control of operators and regulators) and exogenous factors (those factors such as socio-economic influences which are not controlled by regulators/operators) which affect public transport patronage. Suggested barriers include capacity, network transfers, perceptions and investment/subsidy needs. Evidence is presented suggesting that reliability, service levels and fares are the principal tools to adopt in growing patronage. Car ownership, income and population growth, employment and urban sprawl are amongst the exogenous factors identified as influencing patronage.     

International experience with speed limits during and prior to the energy crisis of 1973–4

This paper presents an artificial neural network (ANN) based method for estimating route travel times between individual locations in an urban traffic network. Fast and accurate estimation of route travel times is required by the vehicle routing and scheduling process involved in many fleet vehicle operation systems such as dial‐a‐ride paratransit, school bus, and private delivery services. The methodology developed in this paper assumes that route travel times are time‐dependent and stochastic and their means and standard deviations need to be estimated. Three feed‐forward neural networks are developed to model the travel time behaviour during different time periods of the day‐the AM peak, the PM peak, and the off‐peak. These models are subsequently trained and tested using data simulated on the road network for the City of Edmonton, Alberta. A comparison of the ANN model with a traditional distance‐based model and a shortest path algorithm is then presented. The practical implication of the ANN method is subsequently demonstrated within a dial‐a‐ride paratransit vehicle routing and scheduling problem. The computational results show that the ANN‐based route travel time estimation model is appropriate, with respect to accuracy and speed, for use in real applications.     

Exploring key determinants of travel satisfaction for multi-modal trips by different traveler groups

Using a primary dataset from an experimental survey in eight European cities, this study identified the key determinants of satisfaction with individual trip stages as well as overall journey experience for different travel modes and traveler groups. Multivariate statistical analyses were used to examine the relationships between overall satisfaction and travel experience variables, trip complexity, subjective well-being indices, travel-related attitudes as well as individual- and trip-specific attributes. The results indicate that for certain traveler groups, such as women, young and low-income or unemployed travelers, there are distinctive determinants of satisfaction with trip stages for various travel modes. The results also indicate that satisfaction with the primary trip stage is strongly linked to overall trip satisfaction, while satisfaction levels with access and egress trip stages are strongly related to satisfaction with the primary trip stage. Past experience, traveler expectations and attitudes, and the emotional state of travelers are also significant explanatory variables for travel satisfaction. The results indicate that when an individual consciously chooses a particular travel mode, they will report a higher level of satisfaction with that chosen mode. Notwithstanding, while past experience highly influences an individual’s current travel satisfaction, the more they travel with the current mode, the less satisfied they are with their choice. The results of this study highlight the importance of gaining a better understanding of the interaction between instrumental variables and non-instrumental variables at different trip stages and the influence on user preferences, satisfaction and decision-making processes.     

Route-recording on high resolution transportation network databases for National Transport Surveys: An option for valid and reliable distance measures?

The aim of this study is to investigate the results of route-recording within the Swiss National Travel Survey in the year 2010. The research questions include the following: (1) What level of accuracy and quality of georouting between addresses can be obtained within Computer Assisted Telephone Interview (CATI)-based national travel surveys? (2) What are the differences between estimated and routed distances for stages? (3) Is it worth it to switch from self-estimated distances to technological solutions in national travel surveys with a comparison between GPS and route-recording? It was found that within the method of route-recording, a high share of address-precise geocodes is a necessity for a successful routing fulfilled by the Swiss National Travel Survey 2010. Secondly, route-recording provides reliable and valid distance measures and is one way to overcome the difficulty in participants distance estimates. Thirdly, in its current form, it is not yet an alternative to GPS in national travel surveys, if one is interested in the actual routes travelled by survey participants. Especially there is development work necessary in the routing of non-motorised travel regarding both digital networks and the survey method. The paper concludes by drawing on the findings of this examination and formulating opportunities for improvement and enhancement.     

An exploration of the importance of social influence in the decision to start bicycling in England

The purpose of this paper is to gain a better understanding, through qualitative exploration, of the ways in which social influence affects the decision to start bicycling in England. ‘Social influence’ is defined as the process by which an individual’s thoughts and actions are changed by the thoughts and action of others. Its role was investigated at three levels: the immediate family, household members and significant others (direct social influence); the extended family, friends, peers and colleagues (less direct social influence); and the wider cultural context (indirect social influence). Interviews with 61 individuals living in 12 towns and cities across England were analysed. Half of the interviewees were new regular bicyclists and the other half did not bicycle at all, or only occasionally. Social influence was found to be the dominant factor for a minority of the cases where participants started bicycling regularly. It played a role alongside other factors in other cases. It could take the form of direct influence from family, friends and peers or indirect influence from the social and cultural context. The analysis illustrates the difficulty of capturing social influence which is often hidden and emerges incidentally in the course of the interviews and interacts with other contributing factors. The role of social influence found in this research challenges the rational approach to explaining travel decision making that has traditionally dominated transport studies. The paper suggests that social processes could be harnessed to improve the efficacy of bicycling promotion programs.