Group attitude and hybrid sanctions: Micro-econometric evidence from traffic law

In many legal domains hybrid sanctions – i.e. the joint use of both monetary and non-monetary sanctions – are usually applied. We suggest that one possible rationale behind this form of sanction is targeting group-specific deterrence. For some groups of agents, hybrid sanctions act indeed as a self-selection mechanism such that deterrence is obtained only after a critical threshold of infractions is reached. We apply our model to traffic law infractions and further test it, performing a micro-econometric analysis on a unique dataset of a representative sample of 50,000 Italian drivers, over six years (2003–2009), after the introduction of a penalty points system. Our findings empirically confirm our theoretical predictions. When repeated infractions are at stake, well-designed hybrid sanctions, such as the penalty point system designed for traffic law enforcement, may indeed increase overall deterrence. Our results shed new light on the role of the combined monetary and non-monetary sanctions to perform general and specific deterrence.     

The environmental effects of changing speed limits: A quantile regression approach

Two speed management policies were implemented in the metropolitan area of Barcelona aimed at reducing air pollution concentration levels. In 2008, the maximum speed limit was reduced to 80 km/h and, in 2009, a variable speed system was introduced on some metropolitan motorways. This paper evaluates whether such policies have been successful in promoting cleaner air, not only in terms of average pollutant levels but also during high and low pollution episodes. To do so, we use a quantile regression approach for fixed effect panel data, which allows us analyzing different scenarios (beyond the average levels). We find that the variable speed system improves air quality with regard to the two pollutants considered here, being most effective when nitrogen oxide levels are not too low and when particulate matter concentrations are below extremely high levels. However, reducing the maximum speed limit from 120/100 km/h to 80 km/h has no effect – or even a slightly increasing effect – on the two pollutants, depending on the pollution scenario.     

System dynamics of urban traffic based on its parking-related-states

The urban parking and the urban traffic systems are essential components of the overall urban transportation structure. The short-term interactions between these two systems can be highly significant and influential to their individual performance. The urban parking system, for example, can affect the searching-for-parking traffic, influencing not only overall travel speeds in the network (traffic performance), but also total driven distance (environmental conditions). In turn, the traffic performance can also affect the time drivers spend searching for parking, and ultimately, parking usage. In this study, we propose a methodology to model macroscopically such interactions and evaluate their effects on urban congestion.The model is built on a matrix describing how, over time, vehicles in an urban area transition from one parking-related state to another. With this model it is possible to estimate, based on the traffic and parking demand as well as the parking supply, the amount of vehicles searching for parking, the amount of vehicles driving on the network but not searching for parking, and the amount of vehicles parked at any given time. More importantly, it is also possible to estimate the total (or average) time spent and distance driven within each of these states. Based on that, the model can be used to design and evaluate different parking policies, to improve (or optimize) the performance of both systems.A simple numerical example is provided to show possible applications of this type. Parking policies such as increasing parking supply or shortening the maximum parking duration allowed (i.e., time controls) are tested, and their effects on traffic are estimated. The preliminary results show that time control policies can alleviate the parking-caused traffic issues without the need for providing additional parking facilities. Results also show that parking policies that intend to reduce traffic delay may, at the same time, increase the driven distance and cause negative externalities. Hence, caution must be exercised and multiple traffic metrics should be evaluated before selecting these policies.Overall, this paper shows how the system dynamics of urban traffic, based on its parking-related-states, can be used to efficiently evaluate the urban traffic and parking systems macroscopically. The proposed model can be used to estimate both, how parking availability can affect traffic performance (e.g., average time searching for parking, number of cars searching for parking); and how different traffic conditions (e.g., travel speed, density in the system) can affect drivers ability to find parking. Moreover, the proposed model can be used to study multiple strategies or scenarios for traffic operations and control, transportation planning, land use planning, or parking management and operations.     

A simple algorithm for the estimation of road traffic space mean speeds from data available to most management centres

The control of the evolution of road traffic streams is highly related to productivity, safety, sustainability and, even, comfort. Although, nowadays, the findings from research efforts and the development of new technologies enable accurate traffic forecasts in almost any conditions, these calculations are usually limited by the data and the equipment available. Most traffic management centres depend on the data provided, at best, by double-loop detectors. These loops supply time means over different aggregation periods, which are indiscriminately used as the bases for subsequent estimations. Since space mean speeds are those needed in most applications (note the fundamental relationship between flow and density in traffic flow theory), most current practice begins with an error. This paper introduces a simple algorithm that the allows estimation of space mean speeds from the data provided by the loops without the need for any additional financial outlay, as long as the traffic in each time interval of aggregation is stationary and its speed distribution is log-normal. Specifically, it is focused on the calculation of the variance of the speeds with regard to the time mean, thus making possible to use the relationship between time mean speeds and space mean speeds defined by Rakha (2005). The results obtained with real data show that the algorithm behaves well if the calculation conditions help fulfil the initial hypotheses. The primary difficulties arise with transient traffic and, in this case, other specific methodologies should be used. Data fusion seems promising in this regard. Nevertheless, it cannot be denied that the improvement provided by the algorithm turns out to be highly beneficial both when used alone in the case of stationarity or as a part of a fusion.     

The optimal dispatching of taxis under congestion: A rolling horizon approach

Taxis make an important contribution to transport in many parts of the world, offering demand‐responsive, door‐to‐door transport. In larger cities, taxis may be hailed on‐street or taken from taxi ranks. Elsewhere, taxis are usually ordered by phone. The objective of a taxi dispatcher is to maximize the efficiency of fleet utilization. While the spatial and temporal distribution of taxi requests has in general a high degree of predictability, real time traffic congestion information can be collected and disseminated to taxis by communication technologies. The efficiency of taxi dispatching may be significantly improved through the anticipation of future requests and traffic conditions. A rolling horizon approach to the optimisation of taxi dispatching is formulated, which takes the stochastic and dynamic nature of the problem into account. Numerical experiments are presented to illustrate the performances of the heuristics, taking the time dependency of travel times and passenger arrivals into account.     

Effects of high-speed rail and airline cooperation under hub airport capacity constraint

This paper analyzes the effects of cooperation between a hub-and-spoke airline and a high-speed rail (HSR) operator when the hub airport may be capacity-constrained. We find that such cooperation reduces traffic in markets where prior modal competition occurs, but may increase traffic in other markets of the network. The cooperation improves welfare, independent of whether or not the hub capacity is constrained, as long as the modal substitutability in the overlapping markets is low. However, if the modal substitutability is high, then hub capacity plays an important role in assessing the welfare impact: If the hub airports are significantly capacity-constrained, the cooperation improves welfare; otherwise, it is likely welfare reducing. Through simulations we further study the welfare effects of modal asymmetries in the demands and costs, heterogeneous passenger types, and economies of traffic density. Our analysis shows that the economies of traffic density alone cannot justify airline–HSR cooperation.     

Extended spectral envelope method for detecting and analyzing traffic oscillations

We propose using a spectral envelope method to analyze traffic oscillations using data collected from multiple sensors. Spectral envelops can reveal not only the salient frequencies of periodic oscillations of traffic flow, but also the relative strength of these oscillations at different locations. This paper first introduces time dimension into the existing spectral envelope method so that it can be applied to study the evolution of vehicular traffic oscillations. The extended spectral envelope method proposed in this paper, or ESPE, discards the normalization procedure in the standard method. A new Contributing Index (CI) is proposed to measure the relative strength of oscillations at different locations. The extended spectral envelops can be constructed on long-term or short-term time scales. While the long-term analysis helps extract salient frequencies of traffic oscillations, the short-term analysis promises to reveal their detailed spatial–temporal profiles. ESPE offers two distinctive advantages. First, it is more robust against the impacts of noises. Second, it is able to uncover complicated oscillatory behaviors which are otherwise difficult to notice. These advantages are demonstrated in case studies constructed on both simulated and real data.     

Heterogeneity among motorists in traffic-congested areas in southern California

Estimation of congestion costs, presumed to be one of the largest external costs of automobile travel, is typically based on a single value of time delay for motorists in metropolitan areas. However, the estimation may be wrong if the profiles of motorists are different at different times of day. This study uses a survival model to examine the demographic and socioeconomic profiles of motorists at different times of day at congested locations in southern California, by using on-road remote-sensing measurements and license plates images obtained in 2007 and 2008 by the California South Coast Air Quality Management District (SCAQMD). More than 80,000 vehicles were observed from fifteen selected study sites over fifteen days. Their plates, through anonymized registration records, revealed addresses at the census block group level, which have homogenous profiles by construction. Motorists’ profiles at different times of day display large variation, however, according to extended Cox model with a non-parametric baseline hazard, which is used to accommodate both the time-invariant and time-varying effects of the covariates. This study thus proposes a new approach to examine heterogeneity among motorists.     

Development of origin–destination matrices using mobile phone call data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.     

A network wide simulation strategy of alternative fuel vehicles

This paper presents an integrated simulator “CUIntegration” to evaluate routing strategies based on energy and/or traffic measures of effectiveness for any Alternative Fuel Vehicles (AFVs). The CUIntegration can integrate vehicle models of conventional vehicles as well as AFVs developed with MATLAB-Simulink, and a roadway network model developed with traffic microscopic simulation software VISSIM. The architecture of this simulator is discussed in this paper along with a case study in which the simulator was utilized for evaluating a routing strategy for Plug-in Hybrid Electric Vehicles (PHEVs) and Electric Vehicles (EVs). The authors developed a route optimization algorithm to guide an AFV based on that AFV driver’s choice, which included; finding a route with minimum (1) travel time, (2) energy consumption or (3) a combination of both. The Application Programming Interface (API) was developed using Visual Basic to simulate the vehicle models/algorithms developed in MATLAB and direct vehicles in a roadway network model developed in VISSIM accordingly. The case study included a section of Interstate 83 in Baltimore, Maryland, which was modeled, calibrated and validated. The authors considered a worst-case scenario with an incident on the main route blocking all lanes for 30 min. The PHEVs and EVs were represented by integrating the MATLAB-Simulink vehicle models with the traffic simulator. The CUIntegration successfully combined vehicle models with a roadway traffic network model to support a routing strategy for PHEVs and EVs. Simulation experiments with CUIntegration revealed that routing of PHEVs resulted in cost savings of about 29% when optimized for the energy consumption, and for the same optimization objective, routing of EVs resulted in about 64% savings.