正如指出的那样，精确的计算应该考虑到地球不是一个完美的球体。以下是这里提供的各种算法的一些比较:

geoDistance(50,5,58,3)
Haversine: 899 km
Maymenn: 833 km
Keerthana: 897 km
google.maps.geometry.spherical.computeDistanceBetween(): 900 km

geoDistance(50,5,-58,-3)
Haversine: 12030 km
Maymenn: 11135 km
Keerthana: 10310 km
google.maps.geometry.spherical.computeDistanceBetween(): 12044 km

geoDistance(.05,.005,.058,.003)
Haversine: 0.9169 km
Maymenn: 0.851723 km
Keerthana: 0.917964 km
google.maps.geometry.spherical.computeDistanceBetween(): 0.917964 km

geoDistance(.05,80,.058,80.3)
Haversine: 33.37 km
Maymenn: 33.34 km
Keerthana: 33.40767 km
google.maps.geometry.spherical.computeDistanceBetween(): 33.40770 km

在小范围内，Keerthana的算法似乎与谷歌Maps的算法一致。谷歌Maps似乎没有遵循任何简单的算法，这表明它可能是这里最准确的方法。

不管怎样，这里是Keerthana算法的Javascript实现:

function geoDistance(lat1, lng1, lat2, lng2){
    const a = 6378.137; // equitorial radius in km
    const b = 6356.752; // polar radius in km

    var sq = x => (x*x);
    var sqr = x => Math.sqrt(x);
    var cos = x => Math.cos(x);
    var sin = x => Math.sin(x);
    var radius = lat => sqr((sq(a*a*cos(lat))+sq(b*b*sin(lat)))/(sq(a*cos(lat))+sq(b*sin(lat))));

    lat1 = lat1 * Math.PI / 180;
    lng1 = lng1 * Math.PI / 180;
    lat2 = lat2 * Math.PI / 180;
    lng2 = lng2 * Math.PI / 180;

    var R1 = radius(lat1);
    var x1 = R1*cos(lat1)*cos(lng1);
    var y1 = R1*cos(lat1)*sin(lng1);
    var z1 = R1*sin(lat1);

    var R2 = radius(lat2);
    var x2 = R2*cos(lat2)*cos(lng2);
    var y2 = R2*cos(lat2)*sin(lng2);
    var z2 = R2*sin(lat2);

    return sqr(sq(x1-x2)+sq(y1-y2)+sq(z1-z2));
}

精确计算中长点之间距离所需的函数是复杂的，陷阱也很多。我不推荐哈弗辛或其他球形的解决方案，因为有很大的不准确性(地球不是一个完美的球体)。vincenty公式更好，但在某些情况下会抛出错误，即使编码正确。

与其自己编写函数，我建议使用geopy，它已经实现了非常精确的地理库来进行距离计算(论文来自作者)。

#pip install geopy
from geopy.distance import geodesic
NY = [40.71278,-74.00594]
Beijing = [39.90421,116.40739]
print("WGS84: ",geodesic(NY, Beijing).km) #WGS84 is Standard
print("Intl24: ",geodesic(NY, Beijing, ellipsoid='Intl 1924').km) #geopy includes different ellipsoids
print("Custom ellipsoid: ",geodesic(NY, Beijing, ellipsoid=(6377., 6356., 1 / 297.)).km) #custom ellipsoid

#supported ellipsoids:
#model             major (km)   minor (km)     flattening
#'WGS-84':        (6378.137,    6356.7523142,  1 / 298.257223563)
#'GRS-80':        (6378.137,    6356.7523141,  1 / 298.257222101)
#'Airy (1830)':   (6377.563396, 6356.256909,   1 / 299.3249646)
#'Intl 1924':     (6378.388,    6356.911946,   1 / 297.0)
#'Clarke (1880)': (6378.249145, 6356.51486955, 1 / 293.465)
#'GRS-67':        (6378.1600,   6356.774719,   1 / 298.25)

这个库的唯一缺点是它不支持向量化计算。 对于向量化计算，您可以使用新的gevectorslib。

#pip install geovectorslib
from geovectorslib import inverse
print(inverse(lats1,lons1,lats2,lons2)['s12'])

lat和lon是numpy数组。Geovectorslib是非常准确和非常快!我还没有找到改变椭球的方法。标准采用WGS84椭球，是大多数用途的最佳选择。

function getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2,units) {
  var R = 6371; // Radius of the earth in km
  var dLat = deg2rad(lat2-lat1);  // deg2rad below
  var dLon = deg2rad(lon2-lon1); 
  var a = 
    Math.sin(dLat/2) * Math.sin(dLat/2) +
    Math.cos(deg2rad(lat1)) * Math.cos(deg2rad(lat2)) * 
    Math.sin(dLon/2) * Math.sin(dLon/2)
    ; 
  var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a)); 
  var d = R * c; 
  var miles = d / 1.609344; 

if ( units == 'km' ) {  
return d; 
 } else {
return miles;
}}

查克的解决方案，也适用于英里。

Java实现在根据哈弗辛公式

double calculateDistance(double latPoint1, double lngPoint1, 
                         double latPoint2, double lngPoint2) {
    if(latPoint1 == latPoint2 && lngPoint1 == lngPoint2) {
        return 0d;
    }

    final double EARTH_RADIUS = 6371.0; //km value;

    //converting to radians
    latPoint1 = Math.toRadians(latPoint1);
    lngPoint1 = Math.toRadians(lngPoint1);
    latPoint2 = Math.toRadians(latPoint2);
    lngPoint2 = Math.toRadians(lngPoint2);

    double distance = Math.pow(Math.sin((latPoint2 - latPoint1) / 2.0), 2) 
            + Math.cos(latPoint1) * Math.cos(latPoint2)
            * Math.pow(Math.sin((lngPoint2 - lngPoint1) / 2.0), 2);
    distance = 2.0 * EARTH_RADIUS * Math.asin(Math.sqrt(distance));

    return distance; //km value
}

对于那些寻找基于WGS-84和GRS-80标准的Excel公式的人:

=ACOS(COS(RADIANS(90-Lat1))*COS(RADIANS(90-Lat2))+SIN(RADIANS(90-Lat1))*SIN(RADIANS(90-Lat2))*COS(RADIANS(Long1-Long2)))*6371

源

这个链接可能对你有帮助，因为它详细介绍了使用哈弗辛公式来计算距离。

摘录:

这个脚本计算两点之间的大圆距离 也就是说，在地球表面上的最短距离-使用 “半正矢”公式。

function getDistanceFromLatLonInKm(lat1,lon1,lat2,lon2) {
  var R = 6371; // Radius of the earth in km
  var dLat = deg2rad(lat2-lat1);  // deg2rad below
  var dLon = deg2rad(lon2-lon1); 
  var a = 
    Math.sin(dLat/2) * Math.sin(dLat/2) +
    Math.cos(deg2rad(lat1)) * Math.cos(deg2rad(lat2)) * 
    Math.sin(dLon/2) * Math.sin(dLon/2)
    ; 
  var c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1-a)); 
  var d = R * c; // Distance in km
  return d;
}

function deg2rad(deg) {
  return deg * (Math.PI/180)
}